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17th International Conference on Precision Agriculture and 11th Brazilian Congress on Precision and Digital Agriculture
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AZEVEDO, S.
Abalos, D
Aboutalebi, M
Abud, H.F
Adeyemi, B
Adhikari, K
Affonso Guedes, L
Aflalo, E
Aguiar Jordão, F
Aikes Junior, J
Albuquerque Araujo, G
Albuquerque, B.C
Albuquerque, M
Alchanatis, V
Alegre, J.
Alemandri, V
Alkmim, A
Almeida, D
Almeida, I
Almeida, N
Almeida, R
Alves Filho, R
Alves Henriques, J.P
Alves Soares, F.M
Alves de Araújo, G
Alves de Morais, R.M
Alves, M.N
Alves, R.Q
Alves, S
Alves, T
Alves, T.C
Alvez, R.Q
Alvim Santos Romani, L
Amado, T
Amaral, E
Amaral, L.D
Amaral, L.R
Amaral, M.M
Amaral, S.K
Amaro, R.P
Amstalden, F
Anand, A
Andrade da Silva, A
Andrade, M
Andreoli, A
Antônio, M
Apolo-Apolo, E
Arantes, C
Araujo, E.S
Araujo, G.A
Araújo Barbosa, I
Ard, K
Ardigueri, M
Armstrong, S
Arnosti, M.C
Arruda, P.M
Ata-Ul-Karim, S
Auzani Biscaino, M.L
Avelar, R
Azevedo, I
Azevedo, I.D
BOURLAI, T
Bahat, I
Baker, A.P
Balasundram, S.K
Balboa, G
Balducci Borges, R
Balsamo Brondani, R
Baltazar, J.D
Balzarini, M
Bamberg, D
Barbedo, J.G
Barbosa, I.A
Barbosa, M
Barbosa, P.C
Barboza, T.O
Barioni Jr., W
Barreto, B
Barreto, B.B
Barreto, G.F
Basilio, S
Bassoi, L.H
Bastos, L
Bastos, L.M
Bateman, B
Batista Santos, A
Batista da Silva, W
Battist, R
Baudini, F
Baumgardt, B
Bazzi, C.L
Bedum, G.V
Bendahan, A.B
Bender, E
Bendinelli, W.G
Benetti, A
Benevenuti, F
Bernardes Júnior, E
Bernardi, A
Bernardi, A.C
Bernardo Almeida, E
Bernardo Almeida, E.I
Bernhard, G.B
Berro Filho, C
Berry, P
Beserra, D.V
Bezerra, A.C
Bezerra, C.R
Bhalekar, D
Bharti, D
Bhattarai, A
Biagi, M
Bianchi, R
Bishop, T
Bloch, V
Bobran, K
Bodanese, M
Boeno, D
Bolfe, E
Bonjour, E
Bonomo, J.J
Boote, K
Borah, S
Borges, M.R
Borges, R.D
Botero, J.F
Botta de Siqueira, D.A
Bottega, E.L
Boychyn, J
Brandi, R.A
Brandão, A.D
Brasco, T
Bredemeier, C
Bresilla, T
Bressan, H.R
Brito Filho, A.L
Brook, A
Brorsen, W
Brown, N
Brunetto, G
Bruno, C
Bručienė, I
Buana, I
Buchener, T
Buragiene, S
Buragienė, S
Burgos, N
Butterbach-Bahl, K
C. C. Daher, L
C. R. Seruffo, M
CABRERA DENGRA, M
CARNEIRO FILHO, M.F
CELY BONILLA, E
Cabeza, A
Cabrera Dengra, M
Camargo, S
Camargo, S.D
Cambouris, A
Cammarano, D
Camolesi, A.R
Campos de Oliveira, F.M
Campos, A.F
Canal Filho, R
Canata, T
Canciani, M
Canicatti, M
Cao, Q
Cardoso, P.C
Carmon, T
Carneiro de Souza, L
Carreira, A.D
Carreira, V
Carreira, V.D
Carreno, N.L
Carrer, M.J
Carreño, N
Carrillo Montoya, K
Carter, A
Carvalho de Arruda, D
Carvalho, A.L
Carvalho, F.R
Carvalho, I.R
Carvalho, L
Casas, A.M
Casciello, E
Cassol, V.M
Castanho Fernandes, R
Castillo Ojeda, N
Castoldi, G
Castro, R.D
Cavalcante, W.P
Cavalcanti, R
Cella Ceriotti, V
Centeri, C
Cha Valdez, M.E
Chaer, G.M
Chan Fu Wei, M
Chaparro Anaya, O
Chaves, C
Chen, L
Chen, X
Cherubin, M.R
Chiduwa, M.S
Chimello, L
Chimuando, E.F
Ciampitti, I
Ciancio, N
Cifuentes Arenas, J
Claro, E
Clemente Thom de Souza, R
Coelho, A.L
Coelho, G.P
Cohen, Y
Colaço, A
Colleta de Abreu Moral, P
Colussi, J
Conceicao da Silva, L
Conejo Rodriguez, D.F
Cook, S
Cornejo Noronha, N
Correa, L.R
Corrêa Dalvi, N.
Corrêdo, L.D
Coscelli Rocco, G
Costa Barboza, T
Costa Filho, F
Costa G. da Silva, Y
Costa Linhares, S
Costa Piazza, G
Costa Souza, J
Costa Souza, J.B
Costa Tolfo, A
Costa, B
Costa, B.R
Costa, B.S
Costa, D.D
Costa, N.L
Costa, O.P
Costa, P.S
Costa, R
Costalonga Vargas, B
Craker, B
Crane, O
Cruvinel, P
Cruvinel, P.E
Cruz, O.A
Culda, B
Cunha de Sousa, L
Cunha, I
Cuque, L
Cândido, G.P
Córdoba, M
DE BRITO FILHO, A.L
DE SOUZA Santos, R
Da Costa, O.P
Dalevedo, G.D
Dallegrave, G.D
Dalmolin, R.S
Dandrifosse, S
Danford, D
Dantas Oliveira, S.V
Darghan Contreras, A.E
David, L.C
De Araujo Pedron, F
De Araujo, H
De Faria, C.B
De Guzman, C
De Oliveira Vieira, I
De Ross Marchioretto, L
De Rossi, A
Delgado Bejarano, L
Deponti, L.P
Dhaliwal, A
Dhaliwal, A.K
Dias Borges, R
Dias, E.M
Dias, J.M
Dias, W.V
Didion, T
Diniz Dalmolin, R.S
Doeler, F
Dokoozlian, N
Dong, L
Dong, P
Dos Reis Rodrigues, A.E
Dos Santos Silva, B
Dos Santos, R
Dottori, C
Druyan, S
Duarte, D.S
Duchemin, M
Ductra Bortolotti, G
Dudek, L.F
Duft, D.G
Dujany, A
Dumbá Monteiro de Castro, G
Duncan, W.H
Duque, J
E. Ribeiro, L.C
Echer, F.R
Edan, Y
Eduardo Pereira, C
Eissmann Souza, G
Enderle, T
Eramian, M
Erickson, B
Erthal, L.V
Espindola Muller, L
Espindola Müller, L
Esseili, M
Estrada, E
Evangelista, S.R
Everett, M
FABRO, J.A
FARIAS DO NASCIMENTO, J
FORTES GALLEGO, R
FREITAS DA SILVA, T
FREITAS DO NASCIMENTO, J
Fagundes, F
Fallon, E
Fantin Gebler, H
Fantinel, R.A
Faria, M.A
Faria, R.D
Farias, M.S
Farinati Leite, E
Faucon, M
Faulin, G.D
Favan, J.R
Favarin, J.L
Feld Mikkelsen, B
Feldman, M
Felipe dos Santos, A
Felipe, J.C
Felipe, L.A
Fereres, E
Fernandes Paiva, D
Fernandes Queiroz Alves, R
Fernandes, E
Fernandez, H.J
Ferraz, C
Ferraz, V
Ferreira dos Santos, D
Ferreira e Silva, J
Ferreira, E
Ferreira, E.
Ferreira, E.J
Ferreira, J
Fiechter, C
Fiegenbaum, A.S
Figueiredo, G
Figueiró, A.C
Figueirôa, E.D
Filho, O
Filho, R.
Filippi, P
Fiorio, P.R
Fischer, H
Flugel, L.S
Flynn, K
Flórez Olivera, A.F
Fonseca, A
Fonteca, V
Fontena, V
Fontoura, L.B
Fortes, R
Forti, P.R
Fortinis, H
Franchi, M
Franco Neto, A.R
Franco, G.M
Françani, A.O
Fray da Silva, R
Freire Campos, A
Freire de Oliveira, M
Freitas, A.D
Freitas, D.S
Freitas, E
Freitas, R
Fruchtenicht, D
Fruchtenicht, D.A
Fuhrer, L
Fulton, J.P
Furlan Maggi, M
Furtado Jr, M.R
Furtado Junior, M.R
Furtado Júnior, M.R
Furtado, G.S
Furukawa, H
G. Ferreira, G
G. Gomes, D
G.M. Silva, A
GOMES MESQUITA, D
GUERRA, P
Gabriel da Silva Carmo, I.L
Gabriel, D
Gafni, R
Gaioli Jr, C
Gaion, L.A
Galli, R
Gallo, B.B
Galvan, V.A
Galvan, V.H
Galvão, M.P
Gandorfer, M
Garcia Arnal Barbedo, J
Garcia Dutrez, N
Garcia Ramirez, D.Y
Garcia, A.R
Garcia, P
García Seleme, F
Garnitz, J
Garreto, W
Gaso, D
Gaspareto Filho, C.C
Gatiboni, L
Gavilan, B.Q
Gebler, H.F
Gebler, L
Gelain, M
Genkin, K
Gentili, M
Ghimire, B
Gil da Silva, B.J
Gimenez, L.M
Ginzberg, I
Giroto, V
Glier, C
Gnyp, M
Godinho Silva, S
Godo, A
Golan, R
Goldshtein, E
Gomes Maia, L.K
Gomes Mesquita, G
Gomes, D.G
Gomez-Candon, D
Goncalves, L.M
Gonzalez Aguilera, C
Gonzalez Zarate, O.J
González Zarate, O.J
Gonçalves Junior, S.R
Gonçalves, D.C
Gonçalves, I.D
Gonçalves, L
Gonçalves, L.M
Gonçalves, L.S
Gorthi, S
Grahmann, K
Grando, D.L
Grant, R
Grego, C.R
Griebeler, S.R
Grigas, A
Grünzweig, J.M
Guerra Martins, C
Guimarães Moreira, S
Guimarães, C
Guimarães, E.S
Gupta, A
Gutierrez, S.A
Gyanwali, P
Gómez Montenegro, B
H. S. Sousa, F
HINES PORPINO SANTOS, E
Ha, T
Hall, D.H
Han, E
Hansen, N.P
Harsha Chepally, R
Hass Bomfim Vieira, M
Hatley, D
Hatum de Almeida, S.L
Hatum, S.
Hauschild, M.C
Hawkins, E
Heideker, A
Heller, Y
Henkler, S
Henriques, J.A
Hereman, A.V
Hermes, M
Herrmann Junior, P
Hoffmann Silva Karp, F
Hoffos, B
Hollain, N
Hoogenboom, G
Hosser, M
Hurtado, S.M
Huth, N
Igartua, E
Ikeda, Y
Inamasu, R.Y
Inácio, C.E
Inácio, F.D
Irene, V
Isla Aguilar, A
Itoh, H
Ivo Soares Avelar, R
Jaconis, S.Y
Jakhar, A
Javed, B
Jensen, S.K
Jimenez Lopez, F.R
Jimenez, A
Jin, C
Jin, J
Johari, F
Jorge, L.A
Jotautiene, E
Jotautienė, E
Junior, C.S
Junior, D.U
Jørgensen, J.R
Jørgensen, R.N
Jørgensen, U
Júnior, D.
K. F. Veras, A
KOCH, G
Kabenge, R
Kaefer Seganfredo, G
Kamienski, C
Karam, A
Karasinski, M.A
Karayel, D
Karkee, M
Karp, F.H
Karran, D
Kasita Kashima, F.M
Kastensmidt, F
Kaster Marini, V
Katimbo, A
Katuwal, Y
Katz, L
Kaur Dhaliwal, A
Kaur, D
Kazlauskas, M
Kechchour, A
Keil, F
Keisar, O
Keller, M
Kemp, B
Kern, L.G
Khalid, H
Khan, A
Khanal, S
Khot, L.R
Klinkov, I
Knight, P
Koch, G
Kokkonen, A.A
Komarnisky, Z.C
Kovacs, P
Krco, S
Kriauciuniene, Z
Krohn, N.G
Krumreich, C.R
Kumpatla, S.P
Kusnierek, K
Kyere, I
LEE, J
La Rosa, A
Lacasa, J
Lacerda da Silveira, G
Lacerda, L
Lacoste, M
Lana, M
Landau, A
Langemeier, M
Lanza, P
Lasch, F
Lazzarini, L.V
Leal, G
Leandro, F.H
Leite, D.H
Leite, E.F
Leme, P.M
Lemos, H.R
Lemos, T.F
Lennartsson, E
Levanon, D
Li, Y
Lidor, G
Lima Leal, G
Lima dos Anjos, A
Lima, C.D
Lima, M
Linhares, A.A
Liska, T
Lo Celso, I
Lo, T
Loganathan Girija, D
Logli, M.F
Longchamps, L
Lopes de Brito Filho, A
Lopes, B.V
Lopes, E
Lopes, J
Lopes, T.S
Lopes, W.C
Lord, E
Lotfi, A
Lowenberg-DeBoer, J
Lu, G
Lu, J
Lucas da Silva Ferreira, J
Lucio, B
Luck, J.D
Luiz Panini, R
Luiz de Carvalho, A
Lund, E
Lund, T
Lunewski, C
Luns Hatum de Almeida, S
Luns, S
Luvizotto, C.K
Luvizzoto, C.K
László, M
Lüdtke, L
M. S. de Souza, A
M. Santos, A
M. dos Reis, M
Ma, Y
Macea Zabaleta, L
Macedo, E
Macedo, I
Machado, A.L
Machado, H.A
Machado, L
Machado, R.L
Machado, W.
Maciel Reva, M.A
Maciel, F.N
Maclaren, C
Madsen, M
Madsen, M.S
Maes, W
Maesano, G
Maess, W
Maestrini, B
Magalhaes Cisdeli, P.H
Maidl, F
Maktabi, S
Malacarne, V.H
Maldaner, I
Malone, T
Manjunatha, H
Mantovan, F.D
Marañon Aguilar, E
Marcassa Lonzi de Oliveira, C
Marchetti, J.M
Marchioretto, L.D
Marin, D.B
Mariotto Nabarro, L
Markus, C
Marques, J.R
Martin Carbajal Gamarra, F
Martinez, M.M
Martins Neto, J
Martins, F.R
Martins, J.V
Martins, T.M
Martínez-Guanter, J
Masikati, P
Mason, D
Massruha, S
Matavel, C
Mattar, J
Mattupalli, C
Matwijou, B
Maxton, C
Maxton, C.R
Mazega, M
Mboh, C.M
McCallister, D.M
McCarty, D
McFadden, J
Medeiros, M.
Medeiros, S.R
Medeiros, T.A
Mello, V.S
Melo, D.D
Melo, M.D
Melville, C
Mendes Gaya Lopes dos Santos, I
Mendes, L
Mendes, L.A
Mendes, R
Mendes, R.C
Menegatti, L
Meng, Y
Messiga, A
Meyer-Aurich, A
Miao, Y
Michailidis, A
Michels, M
Miler, C
Milics, G
Mintesinot, S.M
Miranda, G.V
Molin, J.P
Mommen, D
Monachesi, F.P
Monteiro, M
Morais, G
Moreira, M.C
Morgan Pereira, P.H
Morimoto, E
Morlin Carneiro, F
Moro Lumertz, S
Mouazen, A.M
Moura Bueno, J
Moura Bueno, J.
Moura Bueno, J.M
Moura Oliveira, T
Moura Oliveira, T.C
Moura, G.B
Moura, L
Moura, P.A
Moura-Bueno, J.M
Mulla, D
Muller Klassmann, J.V
Mullich, A
Mundada, K
Mundstock, F.D
Murillo Sandoval, P.J
Murumkar, A
Musshoff, O
Mércio, V.Z
Müller, I
Müllich, A
NART MACEDO, W
NUNES, V.M
Naime, J
Nascimbem Ferraz, M
Naujokienė, V
Negrini, R.P
Nelson, K
Ness, Y
Netzer, Y
Neudorf, S
Neupane, S
Nevo, E
Nichols, V.A
Niedbała, G
Nieman, S.T
Nieuwenhuizen
Nishida, K
Nishikawa, M
Niva, N
Nizzoli, A
Njuki Nakabuye, H
Nketia, K
Noal Santarem, M
Nogueira Gusmão, P.H
Nogueira, B
Nogueira, F.I
Novaes da Silva, A
Nunes, A.
Nunes, D.N
Nunes, V.M
Nyagumbo, I
Nze Memiaghe, J.D
Oldoni, H
Oliveira Junior, I
Oliveira, A.L
Oliveira, A.M
Oliveira, D.H
Oliveira, J
Oliveira, J.R
Oliveira, L
Oliveira, M.D
Oliveira, M.F
Oliveira, R.M
Oliveira, R.P
Oliveira, T
Oliveira, T.C
Oliveira, T.M
Oliveira, Z.B
Omar, M.F
Omondi, J
Onyeoguzoro, D
Ortega, I
Ortega, R.A
Ortiz, B.V
Otavio da Silva, E
Otoboni, C.E
Otterson, J
P. M. Nunes, M
PICHORIM, S.F
Paccioretti, P
Paese, B.T
Pagani Neto, N
Paiva, C.M
Pan, D
Pannell, D
Parducci Camargo, T
Pascoaloto, I.M
Passone, N
Paula, G.O
Paulus Scheffer, B
Pavanelli, A
Pavinato, A
Paz Kagan, T
Pedersen, S.M
Pedrosa, A.W
Pegoraro, V.C
Peixoto, A.S
Peixoto, S
Peranzoni Deponti, L
Pereira Costa, G
Pereira da Costa, O
Pereira da Silva, R.P
Pereira da Silva, S.D
Pereira de Morais, E
Pereira, A
Pereira, C.E
Pereira, L.E
Pereira, M
Pereira, M.H
Perez, D
Perez, M.A
Perez, N
Perussi, E
Perussi, E.M
Peternelli, L
Pfrombeck, J
Phillips, S
Pignaton de Freitas, E
Pilcon, C
Pimentel, L.D
Pinet, F
Pinto, F.C
Pitsyk, V
Poblete, H
Polastreli, R.L
Poli, J
Poole, S
Poppiel, R.R
Porcino, T.M
Portelinha, F
Porter, W
Portioli Sampaio, D
Portugal, J.B
Portz, G
Pott, L.P
Poudel, K
Povh, F
Pozzuto, J
Pozzuto, J.V
Prati, R
Prestes Pedroso, T
Previtali, P
Price, A
Proctor, C
Prudenciatto Clemente, E
Puntel, L.A
Pérez-Ruiz, M
Qin, Z
Queiroz, D
Queiroz, R.F
Quicaña, A.
Quinn, D.
Rabello, L.M
Radhakrishnan, A
Rafael Otavio da Silva, E
Ragalzi, C.M
Rai, S
Ramasamy, R.P
Ramos da Silva, G
Ramos, L
Rathore, D
Raucci, A.R
Rayo Álvarez, D
Raza, A
Rebello Pinho Dias Scoton, M.L
Rech, L.F
Reddy Kalluri, R
Regazzo, J
Reginatto, A.C
Reichmann, O
Reis, M.D
Rennó, V
Reusch, S
Reva, M.M
Rezende, P
Rezende, P.S
Rhea, S
Ribeiro Silva, G
Ribeiro, A.D
Ribeiro, B.D
Ribeiro, M
Ribeiro, S
Ricardo Silva Costa, B
Ricci, C.N
Richter, V
Rimoldi Tavanti, R
Riquiel, J.D
Risardi, J
Rivera, F.P
Rocha de Avila, F
Rocha, K.D
Rocha, K.F
Rodigheri, G
Rodolfo, T.A
Rodrigues Moreno, J
Rodrigues Oliveira, J.D
Rodrigues, C.R
Rodrigues, G.C
Rodrigues, L
Rodrigues, M
Rodrigues, M.S
Rodrigues, T.A
Roel, &
Rohlmann, L
Rohrbaugh, K
Rolim Farias da Silva, E
Rolim, G
Rolon, R
Romaneckas, K
Romani, L.A
Ronen, N
Rorato, A
Rosa, S.C
Rosado, T
Ross, J.F
Rossetto Gerlach, L
Rossi, C
Rotbart, N
Roth, R
Rother, K
Roy, A
Rubaino Sosa, S.A
Rubio, G.F
Rubio, J.F
Rudnick, D
Ruge Ruge, I.A
Ruiz Moreno, T
Ruscito, G.N
S. Maciel, T
SERRA BURRIEL, F
SILVA CAVALHEIRO, G
SILVA, R
SOUZA, J
SV, K
Saavedra Rincon, S
Sacomani, R
Sagi, A
Sales, E
Sales, L
Sales, L.D
Salomão, O.D
Salton, A.T
Salvador, I
Sampson, B.J
Sams, B
Sanches, G
Sanches, G.M
Sanches, J
Sanchez Vazquez, G.Y
Sander, L
Sandmann, A
Sandri Sana, R
Santana, C.C
Santos Cocco, T
Santos Comelli da Silveira, L
Santos, A
Santos, A.L
Santos, C.S
Santos, D.J
Santos, F.S
Santos, J
Santos, L.M
Santos, M.F
Santos, P.V
Santos, R.D
Santos, T
Saque Ribeiro, V
Sarri, D
Sausen, M.C
Scali, T
Scaramuzza, F.M
Scarpin, G.J
Scharlau, C.C
Scheeren, I
Scheidt, G.G
Schemmer, S
Schenatto, K
Schilling, K
Schneider, P.S
Schurt, D.A
Secundino, V.C
Serão Filho, M
Sessi, A
Sgarbossa, J
Shapira, O
Sharda, A
Sharma, V
Shearer, S.A
Shibusawa, S
Shirtliffe, S
Shmuel, L
Shovic, J
Shovic, J.C
Shrestha, S
Sijbrandij, F
Silva Costa, B.R
Silva dos Santos, W
Silva e Silva, C
Silva, B.D
Silva, D
Silva, D.O
Silva, E.L
Silva, E.S
Silva, F
Silva, F.D
Silva, F.O
Silva, J.I
Silva, J.P
Silva, L
Silva, L.S
Silva, M.L
Silva, M.S
Silva, R.P
Silva, S.G
Silva, V.S
Silveira Farias, M
Silveira Pavão, L
Silveira de Farias, M
Silveira, A.R
Silveira, D.M
Silveira, G.
Silveira, G.L
Silveira, P
Silveira, S.J
Simons, H
Simwaka, P
Siqueira, G.C
Siquieroli, W.G
Sizemore, J
Smaal, N
Small, I
Smith, D.R
Smith, E
Snir, N
Soares Cardoso, L
Soares de Souza, C
Soares, F
Sobjak, R
Sokas, S
Song, Y
Sorokina, V
Sousa Meneses, E
Sousa Silva, M
Sousa Vieira, M
Sousa, W
Souza Filho, H.M
Souza Pinto, L.S
Souza, B.F
Souza, C
Souza, E
Souza, E.A
Souza, E.G
Souza, J
Souza, J.B
Speranza, E.A
Spricigo, S
Stallivieri, F
Stremel, K
Stroud, T
Su, W
Suarez, F
Subramoni, H
Sundaravadivel, P
Sykes, L.A
Sysskind, M
Szenek, Z
Szám, D
Sánchez-Fernández, L
Sánchez-Gendriz, I
Sárvio Valente, D
Tabbassi, A
Tajidin, N.E
Tamara, A.R
Tamayo López, A
Tamba, H.M
Tamil, L
Tamirat, T.W
Tanajura Caldeira, C
Tanaka, T.S
Tancredi, F.D
Tangerino, G.P
Tavares, A.C
Tavares, T
Tech, A.R
Teixeira Fialho, C.M
Teixeira, C
Teixeira, S.A
Tenenboim, Y
Tesch, C
Tetard, L
Tetila, E.C
Thielemann, L
Thomas, A
Thomé Barbosa, R.N
Tilse, M.J
Toledo, R
Tommaselli, A.M
Tonato, F
Torbert, H
Torre Neto, A
Torre-Neto, A
Torres Avila, E
Tosin, M
Trevizan Paese, B
Tsukahara, R
Tummers, J
Tumwesige, K
Turchiello, J
Tuttle, R
Tyson, C.T
Ugarte, C
Urbina Salazar, D
Usama Bin Sabir, S
Usman, K
Uzoetoh, U
Vacari, I
Vail, B
Valarares, S.V
Valdes Fernandez, G
Valdez, G.F
Valdivino, R
Valente, D.M
Valente, D.S
Valiati, J
Valiati, J.F
Van Der Wal, T
Vasconcellos Lopes, B
Vasconcelos, B.N
Vaz, C.M
Veiga, A
Veldhuisen, B
Vellidis, G
Vergaray Ormeño, C.E
Verçosa, J.P
Viaggi, D
Vian, A.L
Videira Menezes, J
Vidigal Maciel, T
Villegas, D
Villela, J.M
Vinzent, B
Virk, S
Visintainer Lerman, L
Vitor dos Santos, D
Wagner, N.K
Wang, S
Wang, X
Ward, E
Weber, R.K
Weih, M
Weisbjerg, M.R
Weltzien, C
Wendt, L
Wiggins, R
Wilson, J.A
Wilson, J.W
Wing, K
Wojciechowski, T
Wrubleski, M
Wu, Q
Wulff, N.A
Xavier, J.D
Xiaoyu, S
Xu, X
Xu, Z
Xue, H
Yablonski, D
Yan, J
Yang, G
Yatskul, A
Yenibehit, N
Yilmaz, H
Yore, A
Yu, Y
Yılmaz, H
Zakhary, A
Zandonadi, R
Zapata, J.I
Zavala, E
Zavala, E.H
Zhang, J
Zhang, X
Zhao, L
Zhou, Y
Ziadi, N
Zimermam, N.A
Zolin, P.
Zonfrilli, L.E
costa souza, J
da Costa Salem, M
da Cruz, O.R
da Rosa, A
da Silva Brochado, M.G
da Silva Fonseca, J
da Silva Rego, R
da Silva Sousa, W
da Silva, E.C
da Silva, E.F
da Silva, G.B
da Silva, J
da Silva, J.F
da Silva, J.P
da Silva, J.R
da Silva, L
da Silva, L.A
da Silva, M
da Silva, M.A
da Silva, M.M
da Silva, M.P
da Silva, R.F
da Silva, T.R
da Silva, W.B
da Silveira, E.M
de Abreu, J.T
de Albuquerque, B.C
de Almeida, M.C
de Almeida, S
de Almeida, S.L
de Andrade, J.P
de Araújo Pedron, F
de Arruda Viana, L
de Breuil, S
de Carvalho Arruda, D
de Carvalho, H.W
de Castro, A.
de Freitas, A
de Goes Sterle, L
de Lacerda Barbosa, Y
de Lemos, T.F
de Medeiros, R.D
de Mello, P.F
de Novais, V.R
de Oliveira Cavalheiro, H
de Oliveira, D.G
de Oliveira, K.M
de Oliveira, M
de Oliveira, M.F
de Oliveira, R.
de Oliveira, T.M
de Oliveira, V.C
de Paula Amaral, L
de Pinho Alvarez, W
de Queiroz, R.F
de S. Ludovico Almeida, N
de Sousa, P.M
de Souza Pazin, Y
de Souza Salles, E
de Souza Silva, R
de Souza Silva, R.H
de Souza, Z.M
de la Cruz, H.C
do Vale Dondo, A
dos Anjos, J.F
dos Reis Silva, F.O
dos Santos Gonçalves Junior, S.R
dos Santos e Silva, P
dos Santos, N.S
sigdel, U
tamirat, T.W
ten Caten, A
ten Den, T
van der Wal, T
Öborn, I
Šarauskis, E
Topics
Drivers and Barriers to Adoption of Precision and Digital Technologies
Predictive Modeling, Phenotyping, and Digital Tools for Decision Support
Agricultural Robotics, Automation, and Mechanization
Smallholders, Equity, and Social Science Applications
Decision Support Systems, Cloud Platforms, and Open Data Solutions
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Remote and Proximal Sensing of Soils and Crops
UAV-Based Scouting, Imaging, and Targeted Applications
Site-Specific Nutrient, Lime and Seed Management
Digital Solutions for Soil Health, Water Quality, and Conservation Practices
Precision Crop Protection, Pest, and Plant Health
Precision Agriculture for Global Food Security
Precision Agriculture for Sustainability and Environmental Protection
Education, Training, and Extension for Precision Agriculture
Variable-Rate Irrigation, Drainage Optimization, and Water Management
Wireless Sensor Networks, Edge Computing, and Farm Connectivity
Precision Dairy, Livestock, and Animal Welfare Monitoring
Profitability and Success Stories in Precision and Digital Agriculture
Precision Horticulture and Specialty Crop Management
Weather, Climate Models, and Smart Forecasting for Agriculture
Workshops
Invited Presentations
Market Room Sponsors
Meetings
Type
Oral
Poster
Year
2026
Home » Conference » Results

Conference

Filter results448 paper(s) found.

1. 3D Position and Size Estimation of Fruits Using an Intel RealSense D435 Camera

This work addresses the development of a computer vision algorithm for an agricultural robot whose task is to harvest tomatoes in a plantation. The ability to detect fruits, as well as to estimate their 3D position and size in the world coordinate system, is fundamental for accomplishing this task. This approach has been widely discussed in the literature, especially due to the challenge of obtaining accurate estimates of the spatial coordinates of objects. This process is strongly affected b... E. Chimuando, J.A. Fabro, S.F. Pichorim

2. 3D Spatial Resolution of Nutrient and Isotopic Soil Mapping: A Baseline for Precision Soil Management

Although there is a need to increase agricultural productivity, this must occur without compromising soil health and/or surrounding ecosystems and biodiversity. In Westmorland and Furness, England, a feasibility study is being developed to implement a scientific, collaborative, and interdisciplinary strategy involving farmers, scientists, and environmental authorities. The goal is to optimize nutrient management and promote climate-smart agriculture through evidence-based cooperation bet... G.Y. Sanchez Vazquez

3. A Architecture for GNSS-Based Autonomous Navigation in Agricultural Robots

Global Navigation Satellite Systems (GNSS), especially when used with real-time correction techniques such as Real-Time Kinematic (RTK), are widely employed in precision agriculture due to their ability to provide accurate absolute positioning. This capability enables georeferenced operations such as planting, selective spraying, and autonomous navigation across large agricultural areas, even in environments with few structural references. In contrast, modern robotic navigation frameworks, su... V. Fonteca, C. Guimarães, M. Monteiro, I. Azevedo, A. Da Rosa, N.K. Wagner, C. Teixeira

4. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated Corn

Sensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal ... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal

5. A Commercially Feasible Approach to Estimating Spatially Varying Plateau Functions with On-Farm Experiment Data

As technology in precision agriculture continues to advance, unprecedented volumes of high-resolution agronomic data are now available across years, farms, and regions. The availability of this data creates opportunities to improve both the economic and environmental performance of crop production through variable-rate nitrogen (VRN) recommendations tailored to site-specific yield response. However, widespread adoption of VRN remains elusive, in part because existing algorithms are either too... W.H. Duncan, W. Brorsen

6. A Competency Framework for Digital and Precision Agriculture Professionals: Structure and Core Domains

The rapid expansion of digital and precision agriculture technologies has significantly increased the complexity of contemporary agricultural production systems. The integration of technologies—including GNSS-guided machinery, geospatial analytics, sensor networks, artificial intelligence, robotics, and advanced analytics—requires professionals capable not only of operating digital tools, but also of understanding agronomic processes, systems integration, and data-driven decision-... J. Mattar, L. Cuque

7. A Counterfactual Modelling Framework with On-farm Experimentation for Guiding Site-specific Nitrogen Applications

Nitrogen (N) fertiliser is a key driver of wheat grain protein content (GPC) and yield, and represents one of the largest variable input costs and sources of emissions in Australian grain production. Yet estimating optimal N fertiliser rates remains challenging due to spatio-temporal variability in soil N supply and crop nutrient demand, as well as dynamic interactions between yield, GPC, and water availability. On-farm experimentation (OFE) provides valuable insights into crop resp... M.J. Tilse, T. Bishop, S. Poole, P. Filippi

8. A Decentralized Digital Twin Architecture for Interoperable Digital Agriculture Systems

The increasing digitalization of agriculture has driven the widespread adoption of heterogeneous sensing systems, autonomous platforms, and data-driven decision-support tools. Despite these advances, interoperability limitations at both the syntactic and semantic levels remain a major challenge, hindering the scalable integration and coordinated operation of agricultural assets. Current agricultural systems are often developed as vertical, vendor-specific solutions, resulting in fragment... P.H. Morgan Pereira, G. Costa Piazza, K. Usman, L. Santos Comelli Da Silveira, V. Cella Ceriotti, Y. De Souza Pazin, E. Pignaton De Freitas

9. A Decision Support Tool for Developing Aflatoxin Risk Maps in Peanut Fields

Aspergillus flavus (A. flavus) is a soil fungus that contaminates preharvest peanuts (Arachis hypogea) with the carcinogenic secondary metabolite aflatoxin. Because aflatoxin can cause serious illness or death at low concentrations (μg kg-1 to mg kg-1), its presence in foods and feeds is strictly regulated by food safety agencies around the world. Based on previous research by the authors, a hypothesis was developed that aflatoxin ... G. Vellidis, S. Maktabi, K. Boote, G. Hoogenboom, L. Lacerda, C. Pilcon, S. Shrestha, R. Wiggins

10. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers

High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In th... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee

11. A Hardware Classification Matrix for Precision Agriculture: Structuring an On-Farm Living Lab in Brazilian Citrus and Sugarcane Systems

The consolidation of Precision Agriculture (PA) in Brazilian fields depends fundamentally on the physical infrastructure deployed on-farm, including sensors, actuators, embedded controllers, and implements. Although citrus and sugarcane represent pillars of São Paulo's agribusiness, the sector still lacks a systematized inventory that catalogues and classifies PA hardware effectively adopted across different producer profiles. Integrated into the Smart B100 Advanced Research Center... R. Rolon, H. Fischer, C.E. Otoboni, C.K. Luvizotto, M.C. De Almeida

12. A High-Precision Laser Weeding System for Lettuce Fields

Weeds in lettuce (Lactuca sativa L.) fields compete aggressively for essential resources, significantly hindering crop productivity. To enable non-chemical, high-precision weed management, this study developed an integrated laser weeding system leveraging a novel YOLO11-GDCNet algorithm and a compact galvanometer scanning device. The proposed YOLO11-GDCNet enhances the baseline YOLO11n-pose by incorporating GSConv and DualConv modules to reduce computational overhead while improving... W. Su

13. A Hybrid Non-Destructive Approach Combining Image Processing and Spectral Feature Selection for Grapevine Leaf Water Content Estimation

Reliable and continuous estimation of leaf water content (LWC) is essential for viticulture, as it enables assessment of spatiotemporal variability in vine water demand and supports improved irrigation management efficiency within Precision Agriculture (PA) practices. Although the gravimetric method based on fresh weight (FW) and dry weight (DW) measurements provides accurate LWC estimates, it is time-consuming, destructive, and exhibits limited scalability for large sample sizes. In contrast... L.H. Bassoi, B.S. Costa, E.J. Ferreira, H. Oldoni, L.A. Jorge

14. A Machine Learning Framework for Automated Anomaly Detection in Precision Agriculture Geospatial Data

Modern precision agriculture relies on the analysis of geospatial data generated by a wide range of equipment and sensors. While these datasets are foundational to data-driven management practices, they are often affected by inaccuracies arising from various sources. Existing filter systems, such as Yield Editor and Map Filter, that implement operational (e.g., abrupt changes in speed, speed limits, and removal of maneuvers), global statistical (e.g., observations that are inconsistent with t... Z.C. Komarnisky, F. Hoffmann Silva Karp

15. A Machine Learning Framework for Crop Productivity Classification and Risk Assessment

The integration of Artificial Intelligence and Remote Sensing is essential for the early identification of agricultural fields with suboptimal growing conditions. Such capabilities are vital for targeted interventions, supply chain logistics, and agricultural risk management. This study developed and validated a machine learning framework designed to classify the productivity conditions of corn, soybean, and wheat into ‘Low’, ‘Medium’, and ‘High’ tiers, uti... J.D. Xavier, K. Schenatto, G.V. Miranda, C.L. Bazzi, R. Sobjak

16. A Methodological Framework for Modeling Plant Virus Occurrence Using Biometeorological Data: Insights from Multi-crop Case Studies in Argentina

Viral diseases represent a major threat to the productive stability of agricultural systems. Their spatial and temporal occurrence is influenced by environmental conditions that regulate interactions among viruses, vectors, and hosts, making disease anticipation difficult using statistical traditional approaches. This situation highlights the need to understand the dynamics of the different biological components capable of affecting agricultural systems, and design and apply tools that facili... F. Suarez, B. Gómez Montenegro, C. Dottori, V. Alemandri, S. De Breuil, C. Bruno, F. García Seleme

17. A Model to Support Decision-making in the Generation of Management Zones for Fruit Growing

Implementation of precision fruit farming faces challenges in accurately defining these zones, mainly because, as the orchard reaches the productive phase, the relevance of soil fertility decreases compared to other phytotechnical and physiological parameters. Correct generation of management zones is crucial for the success of the operation, but the accurate interpretation of the collected data requires highly qualified professionals with years of experience, a gap that limits the adoption o... L. Gebler, J.M. Dias

18. A Multimodal Spectral-Robustness-LLM Pipeline for Non-Destructive Identification of Loropetalum chinense Cultivars

Proprietary cultivars of ornamental shrub Loropetalum chinense, particularly the visually and spectrally similar ‘Cerise Charm’, ‘Purple Daybreak’, and ‘Red Diamond’, derive their market value from the intensity and stability of anthocyanin pigmentation, a trait that degrades subtly under abiotic stress. Reliance on manual (visual) grading makes the industry vulnerable to these latent, pre-manifestation pigment losses, which are often detected only... P. Sundaravadivel, S. Borah, H. Manjunatha, S.P. Kumpatla, L. Tamil, P. Knight, T. Stroud

19. A New Method for Yield Mapping in Commercial Wine Grape Vineyards of California

Variable rate management (VRM) has been slow to advance in wine grape vineyards, in part due to the lack of commercially available implements or solutions capable of reacting to within vineyard variability. However, the lack of yield maps in vineyards constrain growers’ ability to understand the return on investment, making the cost of VRM difficult to justify. Previous yield monitors mounted on the discharge belts of mechanized grape harvesters were difficult and costly to maintain. A ... B. Sams, M. Aboutalebi

20. A Real-Time Intelligent Framework for Wheat Stripe Rust Management Using Lightweight Deep Learning and LLMs

Wheat stripe rust (Puccinia striiformis f. sp. tritici) poses a severe threat to global food security, necessitating rapid and precise disease grading for site-specific intervention. While edge-computing devices offer on-site monitoring potential, balancing real-time accuracy with cognitive decision-making remains a challenge. This study proposes an integrated intelligence framework that synergistically fuses lightweight visual perception with Large Language Model (LLM)-driven cognitive reaso... W. Su, W. Su, W. Su, W. Su, W. Su

21. A Seeder for Sustainable Agriculture Enabling Intercropping and Multi-Variety Sowing and Adaptable to Precision Agriculture through Variable-Rate Seeding

Davut Karayel*1,2 Egle Jotautiene2 Hasan Yılmaz1,2 1Akdeniz University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Antalya, Turkey 2Vytautas Magnus University, Agriculture Academy, Faculty of Engineering, Department of Agricultural Engineering and Safety, Kaunas, Lithuania. * Corresponding author and presenter   ... D. Karayel, E. Jotautiene, H. Yılmaz

22. A Simplified Optical Sensor-Based Approach for Variable Rate Nitrogen Recommendation Using Vegetation Indices and Yield Potential

Variable rate nitrogen (VRN) management using active optical sensors has been widely studied as a strategy to improve nitrogen use efficiency and reduce environmental impacts. Most current methodologies are based on complex multi-step models that estimate in-season yield from vegetation indices, crop stage and multiple equations, which often results in unrealistic yield predictions and limits adoption by farmers and consultants. This study presents a simplified and robust approach for VR... F. Povh, L.M. Gimenez, L.S. Flugel

23. A Statistical Approach to Defining Coffee Management Zones: Integrating Apparent Soil Electrical Conductivity, Altimetry and Satelitte Indices for Moisture Monitoring

Characterizing the spatial and temporal behavior of soil and plant attributes represents the elementary step toward adoption precision agriculture. The expanding availability of multi-temporal remote sensing imagery with enhanced spatial resolution has rendered the delineation of management zones (MZ) an increasingly feasible strategy, especially when the intention is to carry out spatially differentiated interventions considering the vegetative vigor throughout the crop cycle or the plant yi... E.A. Speranza, E.J. Ferreira, L.H. Bassoi, L.M. Rabello, C.M. Vaz, A. Torre-neto

24. Accessible Spectral Engineering: PLSR Optimized for Organic Carbon Estimation

In the context of Agriculture 4.0, monitoring Soil Organic Carbon (SOC) is crucial for precision agriculture and sustainability, as it is the determining variable for water retention capacity, chemical fertility, and atmospheric carbon sequestration. While the global technological frontier is advancing toward sensors in the Thermal Infrared (TIR) spectrum, adoption in Latin America faces structural barriers of infrastructure and cost. In Peru, the official Walkley-Black method, although norma... A. Isla Aguilar

25. Accuracy Analysis of C/A Code-based GNSS Receivers in Kinematic Condition

Precision agriculture has emerged as a strategic approach to optimize input use and maximize crop productivity. One of the key pillars of this practice is the collection of georeferenced data, essential for the monitoring and efficient management of cultivated areas. This report aims to compare the performance of different positioning signal reception technologies under dynamic conditions. Three C/A code navigation receivers integrated into smartphones, one conventional navigation GPS receive... S. Ribeiro, G. Sanches, C.N. Ricci, J. Regazzo, J.P. Molin

26. Adapt Standard: Enabling Interoperability in Agricultural Field Operations Data

Modern agriculture increasingly relies on sophisticated technologies, including precision farming equipment, sensors, laboratory analyses, and farm management software, to generate critical operational data. Despite these advancements, the industry faces significant interoperability challenges, resulting in fragmented data ecosystems that impede optimized decision-making. While ISO 11783 (ISOBUS) successfully facilitates electronic communication at the machinery level, it does not adequately ... B. Craker, S.T. Nieman, J.W. Wilson, S. Rhea, K. Nelson, D. Danford, J.A. Wilson, B. Kemp

27. Adding Plant Water Uptake Monitoring to a Low-Cost Wireless Multi-Sensor Data Aggregation Platform for Precision Agriculture

Most low-cost precision agriculture monitoring systems only measure soil conditions such as moisture and pH, leaving plant response to water availability unmonitored. Soil moisture alone does not indicate whether a crop is taking up water or experiencing stress. Commercial plant-sensing systems that could fill this gap are expensive and operate as standalone instruments that are difficult to incorporate into existing multi-sensor field networks. ... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic

28. Advanced 2D and 3D Image-Based Plant Phenotyping of Citrus Morphological Responses to Candidatus Liberibacter Asiaticus Infection

Huanglongbing (HLB), associated with Candidatus Liberibacter asiaticus (CLas), is the most destructive citrus disease worldwide and threatens the long-term sustainability of production because all commercially cultivated varieties are susceptible. Identifying tolerant or resistant genotypes has therefore become a central priority for modern citrus breeding programs. Conventional phenotyping based on visual scoring and manual measurements is time-consuming, labor intensive, s... J. Cifuentes Arenas, C. Lunewski, F. Keil, M.N. Alves, N.A. Wulff

29. Advanced Method to Assess the Impact of Soil Fertility Variability on Maize (Zea mays L.) Productivity.

Maize production in Brazil for the 2024/2025 season is estimated at 119.6 million tons, representing a 3.4% increase compared to the previous cycle, despite a 5.9% reduction in cultivated area. This scenario highlights the need for more efficient agricultural systems capable of sustaining high productivity levels under land-use restrictions. Traditionally, high-yield agriculture has relied on intensive input use, a practice that can lead to waste. In contrast, precision agriculture emerges as... D. Portioli Sampaio, J.M. Villela, P.E. Cruvinel

30. Africa Regional Meeting

... K. Nketia

31. AgDataBox-Map: Web Application for Spatial Analysis and Delineation of Management Zones for Dynamic Fruit Harvesting in Precision Agriculture

Harvest maps are fundamental tools in precision agriculture, as they allow for the evaluation of whether corrective actions taken before and during the harvest have had the planned effect, through visualization of the heterogeneity of production within the cultivated area. However, in fruit growing, due to the extensive use of manual labor for harvesting to the detriment of mechanization, the generation of thematic maps has always been a problem. In Brazil, initiatives developed by the Federa... C.B. De Faria, L. Gebler, L. De Ross Marchioretto, C.L. Bazzi

32. AgGeoSampler: A Geospatial Open-Source Data Acquisition and Sampling Design Dashboard for Agricultural Applications

Modern agricultural and environmental research increasingly depends on high-resolution geospatial data to support precise, site-specific decision-making. Advances in satellite remote sensing, unmanned aerial systems, and digital soil mapping have generated vast spatial datasets that capture fine-scale variability in vegetation health, soil properties, and terrain attributes. However, translating this wealth of information into effective field-sampling... A. Bhattarai, A. Jakhar, K. Poudel, A. Dhaliwal, L.M. Bastos

33. Agro Extensão: Extensão Rural Digital

University extension plays a fundamental role in bridging the gap between academia and society, especially in the agricultural sector, where technical information can determine the success of an operation. A historical example of this relevance is Operation Tatu (1960), which was essential in transforming the agricultural model in Rio Grande do Sul, promoting the shift from rudimentary practices to a technological approach. This initiative became a landmark demonstration of the potential of u... F. Baudini, A.L. Vian, M. Wrubleski, T. , G. Lima Leal

34. Agroclimatic and Topographic Zoning for the Sustainable Expansion of Peanut Production in the State of Georgia, USA

Sustainable agricultural production depends on a detailed analysis of environmental conditions to support decision-making. This study aimed to develop a topoclimatic zoning for peanut production in Georgia, USA, using climatic data from the PRISM Climate Group and topographic data from OpenTopography. The water deficit was calculated using the Thornthwaite and Mather methodology. The methodology included the reclassification of variables into three suitability classes for cultivation, based o... I. De Oliveira Vieira, S. Luns, R.C. Mendes, L. Bastos, R.P. Silva

35. Agronomic and Economic Performance of Early Maize and Weed Management Under Agriculture 4.0 versus Conventional Systems

Agriculture 4.0 stands as a crucial strategy to optimize operational efficiency and environmental sustainability in maize cultivation, enabling rationalized input use through automation and data management. However, limited comparative data exist regarding its agronomic efficacy against conventional practices under tropical conditions. Thus, this study aimed to compare vegetative development and weed infestation in conventional and Agriculture 4.0 cropping systems. The experiment was conducte... P. Colleta De Abreu Moral, L.A. Gaion, C.C. Gaspareto Filho, I.M. Pascoaloto, E. Fernandes, J. , T.F. De Lemos

36. Agronomist-in-the-Loop Semantic 3D Reconstruction of Cotton Boll Morphology from UAV Imagery for Precision Agriculture

Standard aerial photogrammetry is failing precision agriculture in one specific area: the detailed morphological assessment of complex, cluttered canopies. While creating a field-level map is trivial, recovering the geometry of a single cotton boll from a drone altitude of 30 meters is often mathematically intractable for standard Structure-from-Motion (SfM) solvers. These traditional pipelines depend on pixel-perfect consistency, which breaks down am... P. Sundaravadivel, H. Manjunatha, S. Borah, A. Anand, A. Price, H. Torbert, L. Tamil, T. Stroud

37. Alternative Method for Measuring Fuel Consumption in Agricultural Machinery Using Arduino and Flow Sensors

Monitoring fuel consumption in agricultural machinery is a strategic component of precision agriculture, as it is directly associated with operational efficiency, cost reduction, and the mitigation of CO₂ emissions. Despite technological advances in agricultural tractors, most machines, including recent models, do not feature dedicated sensors for direct fuel flow measurement, relying instead on visual fuel level indicators or estimates based on engine parameters, which limits the accuracy ... R. De Souza Silva, L.E. Zonfrilli, A. Andrade Da Silva, R.P. Silva, A.D. Carreira

38. An AI-Ready Smart Adapter Architecture for Integrating Heterogeneous Agricultural IoT Systems Across the Edge–Cloud Continuum

The increasing adoption of Internet of Things (IoT) technologies in smart agriculture has resulted in highly heterogeneous environments composed of diverse sensors, communication protocols, and distributed computing layers. Agricultural systems typically operate across the edge–cloud continuum, encompassing field devices, intermediate processing nodes, and cloud-based platforms. While IoT platforms provide essential services for data ingestion and device management, they often face limi... D. Silva, A. Heideker, R. Bianchi, C. Kamienski

39. An Integrated Water–Energy Vulnerability Index for Irrigated Agricultural Regions

The growing interdependence between water availability and energy infrastructure has significantly increased the vulnerability of irrigated agricultural regions, particularly under conditions of climate variability, hydrological uncertainty, and seasonal demand peaks. Irrigated production systems simultaneously depend on reliable water supply and stable energy provision, making them particularly sensitive to disruptions in either domain. Although the water–energy nexus literature has ad... T.A. Rodolfo, P.S. Schneider, M.A. Perez, F.D. Mantovan, H.R. Bressan, A.C. Reginatto

40. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical Conductivity

Understanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration ... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera

41. An Online Decision Support Tool for Homogeneous Zone Delineation in Precision Agriculture

Management zone delineation is a key component of site-specific management in precision agriculture, enabling the spatial optimization of inputs and an improved understanding of within-field variability. Traditionally, homogeneous zones have been derived from historical yield maps or soil-related variables obtained through proximal sensing. More recently, the increasing availability of multispectral satellite imagery and derived vegetation indices has expanded the range of data sources availa...

42. An Open-Source, Universal Arduino Library for LoRaWAN Integration with Low-Cost, Long-Range Agricultural Sensor Networks

The Data-Gator is a low-cost, open-source sensor platform built on the ESP32 microcontroller. It supports I2C, analog, and Bluetooth Low Energy (BLE) sensor interfaces and can be configured without writing any code. It has been tested at a vineyard and an organic orchard, where it collects readings from a number of sensors at regular intervals. While the platform performs well in these settings, it currently relies on WiFi to send data back from the field. This l... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic

43. Analysis of Mixed Models in UAV-based Spectral Vegetation Indices for Prediction of Agronomic Variables in Soybean Subjected to Flooding

The identification of soybean genotypes with increased flooding tolerance is relevant for yield stability in lowlands producing areas. In this context, the use of relevant spectral vegetation indices based on multispectral sensors embedded in unmanned aerial vehicles (UAVs) for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics. Nonetheless, the environmental effects can change the accuracy of spectral indices and the correct methodology for d... C.D. Lima, B. Nogueira, A. , D.U. Junior, I.R. Carvalho, C. Bredemeier

44. Application for Pixel-level Segmentation and Quantification of Lignified Fibers (Sclerenchyma) and Parenchyma in Microscopic Images of Sugarcane Culms

Quantifying lignified tissues in sugarcane culms (Saccharum spp.) is essential for anatomical characterizations and for inferences related to biomass quality and the potential uses of plant material. Conventional methods may require specific laboratory procedures and manual steps in digital analysis, increasing processing time and reliance on skilled operators. In this context, computer vision techniques applied to microscopic images constitute an accessible and reproducible alternat... G.F. Rubio, J.F. Rubio, L.E. Pereira, J.R. Marques, A.R. Tech, M.F. Logli

45. Application of CNNs in Cattle Counting using RPAs

The increasing demand for productive efficiency and sustainability in the agricultural sector has driven the adoption of technologies focused on Precision Livestock Farming. Among the main operational challenges in extensive systems, the counting and monitoring of cattle herds stand out. Historically performed manually, these activities are time-consuming, increase labor costs, and are highly susceptible to human error, especially across vast territorial expanses. However, the parallel advanc... E. De Souza Salles, C. Souza, R. Clemente Thom De Souza

46. Application of Machine Learning Algorithms and Remote Sensing for Predicting Losses in Peanut Harvesting

Peanut (Arachis hypogaea L.) is a crop of substantial economic and social relevance in Brazil, particularly in the state of São Paulo, which accounts for the majority of national production and consistently attains high productivity levels. Despite significant advances in agricultural mechanization, harvesting remains one of the most critical phases of peanut production, especially during mechanical digging, a stage in which considerable yield losses frequently occur. These losses are ... G. Pereira Costa, A.L. Brito Filho, T.C. Oliveira, J. , R.P. Silva

47. Applying Precision Agriculture Principles to Assess Spatial Variability of Soil Fertility Profiles in Coffee Farms at a Regional Scale.

In the last two decades, the development and increasing efficiency of Precision and Digital Agriculture technologies has been observed in a broad range of farming systems. One of the consequences of this was the popularization of digital data collection, resulting in the current scenario where agricultural companies usually have large georeferenced databases concerning climate, soil and plant traits. These datasets are also produced by agricultural cooperatives such as the Cooxupé (Reg... H.F. Gebler, B.R. Silva Costa, J.P. Molin

48. Are Agronomy Programs Preparing Professionals for Digital Agriculture? A Nationwide Curriculum Analysis in Brazil

The digital transformation of agriculture has accelerated the adoption of precision agriculture, artificial intelligence, and data-driven management tools, thereby increasing the demand for professionals equipped with technological and computational competencies. In this context, higher education in Agronomy plays a strategic role in preparing graduates to operate effectively in increasingly digitalized production systems. This study aimed to evaluate the presence of Artificial Intelligence (... L. Silva, D. Gabriel

49. Artificial Intelligence for Management Zone Delineation: A Bibliometric Review (2008-2025)

This bibliometric review aims to map research trends, key terms, and leading institutions in the use of artificial intelligence (AI) methods, with emphasis on Machine Learning (ML), Deep Learning (DL), and Neural Networks, applied to the delineation of management zones (MZs) in Precision Agriculture (PA). The analysis was conducted using the Scopus database, applying a structured search string to titles, abstracts, and keywords. Metadata were collected on September 26, 2025. The initial searc... L. De Goes Sterle, J.P. Molin, R. Fray Da Silva

50. Artificial Intelligence Framework for Bioenergetic Flow Audit and Thermal Entropy: A Precision Approach in the Brazilian Semiarid

Extensive livestock farming in the Brazilian semiarid faces productivity bottlenecks masked by weed competition, where invasive plants mimic vegetative vigor but impose thermal and nutritional stress on the herd. This study aimed to develop and validate the "Predict-IA" framework, a bioenergetic audit tool based on Artificial Intelligence to quantify systemic entropy and "energy leakage" in degraded pastures. A ten-year time series (2015-2025) of Sentinel-2 multispectral d... A. Alkmim, D. Vitor Dos Santos

51. Artificial Intelligence in Precision & Digital Agriculture

... S. Massruha

52. As-applied Maps of Planter Performance During Corn Planting – How the Numbers Looks Like After Plant Emergence

High-quality planting operations are important for high yield crops. Poor distribution compromises emergence and plant development, thus affecting crop productivity. Therefore, uniformity of seed distribution in the soil, with adequate depth and spacing, is essential for high yield. Moreover, embedded electronics on modern planters allow data collection from sensors that provide information regarding planter performance such as measurements of seed spacing quality (doubles, skips, singularity... D. Ferreira Dos Santos, S.C. Rosa, R. Zandonadi

53. Asia and Oceania Regional Meeting

... S.K. Balasundram

54. Assessing Metering and Spreading Performance of Drones (UAVs) for Application of Dry Materials

Along with pesticide applications, the use of UAVs (also commonly referred to as drones) for applying dry materials has increased rapidly in the United States. Currently, various types of product metering and spreading systems are available on commercial drones for applying dry materials. However, limited information is available on their application performance, especially the metering accuracy and the uniformity of distribution across the swath. Therefore, research studies were conducted to... S. Virk, E. Ward, J. Sizemore

55. Assessing Soil Fertility Inequality at Regional Scale to Support Plot-level Site-specific Management

In accordance with Precision Agriculture (PA) principles, site-specific management could be performed in small-scale farming systems, assuming a cell-size approach where between-plot variability is manageable, rather than the within-plot variability. This framework is particularly useful for fertilization strategies within a single farm and may be extended to a macro scale when georeferenced datasets from multiple plots and farms across a region of interest are available. However, when dealin... B. Costa, B.B. Barreto, H. Fantin Gebler, J.P. Molin

56. Assessing the Capability of Apparent Soil Electrical Conductivity to Replace Soil Texture in the Delineation of Management Zones: a Case Study

The delimitation of management zones (MZ) depends on the appropriate selection of information layers used in the clustering process. Traditionally, soil physical attributes with greater temporal stability, such as texture, have been widely employed due to their direct influence on water retention and nutrient availability. However, the acquisition of soil texture data is generally costly, time-consuming, and based on point sampling. In contrast, apparent soil electrical conductivity (ECa) has... M. Gelain, L. De Goes Sterle, J.P. Molin

57. Assessing the Potential of Google Satellite Embeddings for Mapping Sugarcane in Brazilian Production Areas

The increasing availability of remote sensing (RS) data with higher spatial resolution, combined with advances in artificial intelligence (AI), has been an essential tool in driving the development of the agricultural sector, such as precision agriculture (PA). Among the most relevant information derived from these approaches, crop mapping plays an essential role in crop monitoring, management strategies and yield forecasting. However, the large amount of data required for training classifica... G. Rodigheri, J. Da Silva, L. Alvim Santos Romani, J. Garcia Arnal Barbedo

58. Assessment of Machine Learning Models for Leaf Chlorophyll Estimation Using Visible-Range Reflectance

Chlorophyll content plays a central role in the photosynthetic process directly influencing plant growth, development and yield. However, plant pigment dynamics arise from complex metabolic interactions that are not adequately captured by conventional statistical approaches or traditional laboratory analyses, which are time-consuming and impractical for large-scale field applications. In this context, remote sensing offers a non-destructive alternative for assessing foliar pigments in agricul... T. Costa Barboza, W. Batista Da Silva, S. Guimarães Moreira, S. Godinho Silva, L. Lacerda, A. Felipe Dos Santos

59. Assessment of Spatiotemporal Variability in Desiccation Efficiency Using a Spray Drone Through Vegetation Indices

The increasing adoption of spray drones in precision agriculture has raised important questions regarding operational parameters and their influence on herbicide performance under field conditions. Although unmanned aerial spraying systems offer advantages such as reduced soil compaction, greater operational flexibility, and rapid field coverage, the interaction between flight parameters and droplet deposition dynamics remains insufficiently understood. This study aimed to evaluate the spatia... G. Lacerda Da Silveira, M.C. Arnosti, G. Valdes Fernandez , T. Costa Barboza, A. Felipe Dos Santos, E. Amaral

60. Automated Detection of European Canker (Neonectria ditissima) in Apple Trees via Multispectral Sensors and Computer Vision

European canker, caused by the fungus Neonectria ditissima, represents one of the major economic challenges for Brazilian pomiculture, severely affecting ‘Gala’ and ‘Fuji’ cultivars. The disease manifests primarily in woody tissues, such as trunks and branches, although it can also cause fruit rot during the pre-harvest stage. Infection occurs obligatorily through wounds, whether natural (leaf scars) or resulting from management practices (pruning and harvesti... S. Alves, E.C. Da Silva, L.D. Marchioretto, L. Gebler

61. Automated Detection of Melons (Cucumis melo L.) via Multispectral UAV and Deep Learning in Honduras

Accurate agricultural production estimation is vital for the logistical and financial efficiency of agribusiness. This study proposes an automated melon detection pipeline using a multispectral Unmanned Aerial Vehicle (UAV) and deep learning architectures. The experiment was conducted in Apacilagua, Honduras, during the 2023–2024 season, covering an area of 32.17 ha. Data collection took place between 90 and 100 days after sowing (DAS)—a critical maturation phase—using a DJI... E. Torres Avila, C.L. Bazzi, S. Moro Lumertz, E. Cely Bonilla, M. Furlan Maggi, T.A. Medeiros, D. Perez, K. Schenatto, R. Sobjak

62. Automated Initial Plant Stand Assessment in Bean Crops Using Uav-based Yolov8 Detection

The use of RGB images acquired by unmanned aerial vehicles (UAVs), combined with artificial intelligence techniques, has increased significantly in recent years for object identification and crop monitoring in agriculture. These technologies enable rapid plant stand count, facilitating decision-making processes. However, limited information is available regarding the optimal flight height for identifying bean plants at early growth stages. Therefore, the objective of this study was to evaluat... G. Valdes Fernandez , G. Lacerda Da Silveira, R. Fernandes Queiroz Alves , T. Costa Barboza, M.C. Arnosti, A. Felipe Dos Santos, W.B. Da Silva, O. Pereira Da Costa

63. Automated Leak Classification in Drip Irrigation Systems using Deep Learning and RGB Cameras

The increasing demand for water efficiency in agriculture has driven the development of intelligent irrigation systems.  Among them, drip irrigation is widely adopted due to its efficiency;  however, these systems are susceptible to leaks caused by mechanical wear, animal interference, and adverse environmental conditions. The manual detection of leaks by human workers in drip irrigation systems is a time-consuming task,  difficult to scale, ... F.P. Rivera, C. Kamienski

64. Automatic Creation of Thematic Maps and Management Zones Using Agdatabox-fast Track

Precision agriculture encompasses the strategic application of inputs in requisite quantities at optimal times to enhance overall productivity. An essential aspect of this methodology is the formulation of thematic maps (TMs) and management zones (MZs). Despite their critical importance, delineating TMs and MZs requires substantial technical expertise in their construction, making their application challenging, particularly for smaller producers, due to the need for a specialized multidiscipl... J. Aikes Junior, E. Souza, C.L. Bazzi, R. Sobjak, E. Souza

65. Automatic Detection of White Shrimp (Litopenaeus Vannamei) Feeding Activity Using Acoustic Signals

In the cultivation of white shrimp (Litopenaeus vannamei), feeding management is one of the main challenges, accounting for approximately 40% to 60% of operational costs. Inaccurate feed management not only increases production costs but also compromises water quality, leading to environmental impacts. Shrimp produce acoustic events known as clicks, which makes it possible to use these signals as indicators of feeding activity. This study analyzes acoustic data collected ov... F. Costa Filho, L. Affonso Guedes, S. Peixoto, I. Sánchez-gendriz

66. Autonomous Edge Computing Station for Precision Soil and Climate Monitoring in Remote and Low-Connectivity Environments

The expansion of Precision Agriculture (PA) into remote rural areas, particularly in developing countries like Brazil, is frequently hindered by severe infrastructure constraints. Large-scale adoption of digital tools faces the dual challenge of limited 4G/5G connectivity at the field level (the "talhão") and the high costs associated with conventional electrification and industrial-grade equipment. These barriers disproportionately affect small and medium-sized farmers, crea... M. Andrade, R. Toledo

67. Autonomous Edge-AI–Enabled Drone Systems for Real-Time Agricultural Inference and Decision-Making

High-throughput, low-latency phenotyping and field surveillance remain critical bottlenecks in precision agriculture and environmental monitoring due to delayed data turnaround, large data volumes, computationally intensive preprocessing, and expertise-heavy analysis workflows. These constraints hinder timely crop improvement, pest and disease management, and informed agronomic decision-making. To address these challenges, we present an integrated, end-to-end autonomous drone system that enab...

68. Autonomous Mobile Robot for Monitoring and Control of the Cotton Boll Weevil

The cotton boll weevil (Anthonomus grandis Boheman) is the key pest of Brazilian cotton, accounting for about 12% of production costs and, together with yield losses, reaching roughly R$ 2,470 per hectare per season. Because its immature stages develop protected inside the plant, insecticides reach only the adult, which entrenches calendar-based broadcast spraying and an average of 18 full-field applications per season. Conventional scouting samples as few as 0.1 points per hectare, leav... R. Balducci Borges, N. Smaal, P.V. Santos, W.G. Bendinelli

69. Autonomous Robotic Spraying System for Weed Management in Woody Perennial Crops

Weed management in woody crops remains a major agronomic, economic, and environmental challenge. In orchard environments, tree trunks, low canopies, and narrow intra-row spacing severely restrict conventional machinery access to the under-canopy zone. As a result, weed control near tree trunks remains predominantly manual. This limitation coincides with an increasing scarcity of agricultural labor, leading to higher production costs and delays in weed... M. Pérez-ruiz, L. Sánchez-fernández, A. Nizzoli, E. Apolo-apolo, J. Martínez-guanter

70. Benchmarking Precision Agriculture Adoption in the United States and Brazil

The United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted a... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter

71. Beyond the Mean: A Quantile Count Regression Analysis of Precision Farming Technology Adoption Intensity by German Farmers

This study examines the factors influencing precision agriculture technology adoption intensity among German farmers using an innovative quantile count regression approach that reveals heterogeneous relationships across different segments of the adoption distribution. While previous research has primarily relied on mean-based regression models that may mask important variation in adoption determinants, this analysis provides new insights into how factors affect low, moderate, and high technol... M. Michels, O. Musshoff

72. BOSCH - The Invisible Cost of Variability: Where Smart Agriculture Generates Value

... N. Almeida

73. Bridging Continents for Soil Carbon Mapping: A Transfer Learning Framework from European LUCAS to Chinese Farmlands

Soil organic carbon (SOC) mapping is fundamental to precision agriculture and climate change mitigation. Building accurate SOC prediction models typically requires extensive local sampling, which is costly and time-consuming. Can spectral-SOC relationships learned from large-scale soil databases be transferred across continents? This study addresses this question by developing a transfer learning framework that leverages the European LUCAS database (n=2288) to predict SOC in Chinese farmlands... X. Chen, Q. Cao

74. Caliming: A Decision-Support Tool for Soil Liming Recommendations

Soil acidity is one of the main limiting factors for crop production in tropical regions. In this context, liming stands out as the most efficient strategy for correcting soil acidity, promoting pH increase, neutralization of toxic aluminum, and the supply of calcium (Ca) and magnesium (Mg) to plants. Several methods for calculating lime requirement have been developed over time, ranging from classical approaches consolidated in official recommendation handbooks to more recent methods, such a... G. Castoldi, C.R. Rodrigues, E.S. Araujo, S.K. Amaral

75. Can Management Zones Be Useful in Guiding Mechanized Peanut Harvesting?

Mechanized peanut harvesting presents challenges due to the crop’s indeterminate growth habit, which results in non-uniform maturation across the field; the ideal harvest point is reached when the maturity index exceeds 0.7. Consequently, analyzing the spatial variability of maturation is essential for identifying homogeneous areas and guiding harvest at the optimal time. In this context, Management Zones (MZs), as a Precision Agriculture tool, enable the subdivision of fields into more... A. Andrade Da Silva, T.C. Moura Oliveira, E. Sales, S. Luns, E.M. Perussi, R.H. De Souza Silva, S. Luns, R.P. Silva, J. , A.L. De Brito Filho , R.P. Silva

76. Can Soil Profile Mapping and AI‑Assisted Yield Analysis Improve Explanation of Within‑Field Yield Variability? A Depth‑Resolved Case Study

Within-field yield variability is commonly evaluated using surface soil test results and whole-field statistical correlations. However, these approaches often fail to explain why areas with acceptable surface pH and nutrient levels continue to underperform, while neighboring zones with similar or even lower soil test values yield substantially better. This study evaluates whether depth-resolved soil profile characterization can improve diagnosis of spatial yield variability. Rather ... E. Lund, T. Lund, C. Maxton

77. Carbon Stock Assessment in Macaúba (Acrocomia Aculeata) Crops Based on Aerial Digital Images

In the current context of climate change, a palm tree named Macaúba, native to the Brazilian Cerrado, has gained prominence as a more sustainable alternative to oil palm, standing out for its high capacity to fix atmospheric carbon throughout its cycle. However, there is a lack of methodologies capable of quantifying carbon stocks in large-scale plantations in a cost-effective way, and manual sampling is still common. In this context, the main objective was to evaluate the effectivenes... P.M. De Sousa, B.C. De Albuquerque, V.A. Galvan, L.D. Corrêdo, L.D. Pimentel, J. Souza

78. Challenges in Adapting Precision Agriculture to Specific Contexts in Latin America

... J.P. Molin

79. Challenges in Integrating Digital Agriculture Solutions

Advances in digital agriculture have increased the supply of solutions to improve the management of agricultural activity. However, the increasing number of solutions in quantity and variety also imposes barriers to their adoption by small and medium-sized family farmers reasoned by higher exposition to technical and financial limitations. High cost, low digital literacy, and little perception of the usefulness are some of the obstacles. These can be further exacerbated if producers need ... J. Da Silva, S.R. Evangelista, J.G. Barbedo, L.A. Romani

80. Characterization of Spray Application with a 110015 Ad Nozzle Using a Remotely Piloted Aircraft: Evaluation of Flight Altitude and Collector Type

Remotely piloted aircraft (RPA) spray systems represent an innovative technology in modern agriculture, offering pesticide application with high precision and operational efficiency. A comparative study of different collector substrates is essential for precision agriculture, as each substrate exhibits specific droplet absorption and retention properties that significantly affect the evaluation of spray performance and pesticide deposition efficacy. Investigating the interaction of sprayed dr... R.F. De Queiroz, P.S. Rezende, A.M. Oliveira, G.P. Tangerino, A.R. Franco Neto

81. Characterizing Cross-Crop Stink Bug Spectral Signatures from Hyperspectral Data

Effective crop protection in agricultural production systems requires the ability to detect pest-induced stress in a timely and reliable manner. In large-scale farming systems, stink bugs attack multiple crop species, making cross-crop pest detection a critical capability for scalable monitoring solutions. Rather than developing crop-specific models that require retraining for each species, identifying crop-independent spectral signatures of stink bug infestation enables transferable detectio... A.O. Françani, L. Zhao, J. Ferreira , J. Yan, E.J. Ferreira, L.A. Jorge

82. Climatic Zoning of the Peanut Cercosporiosis Complex in São Paulo Under Climate Change Scenarios

The cercosporiosis complex is an important foliar disease of peanut, caused by the fungi Cercospora arachidicola and Nothopassalora personata, impacting grain yield and quality. Another relevant aspect is the symptoms of defoliation and vegetative weakening caused by these fungi, which may lead to significant losses during the digging and harvesting stages of peanut, a crop intrinsically associated with mechanization. The objective of this study was to develop a climatic zoning of the cercosp... R. Mendes, I. De Oliveira Vieira, R.P. Silva

83. Co-registration of RGB UAV Orthomosaics Through a Semi-automated Affine Method Based on Ground Control Points and Phase Correlation Validation

UAV images are crucial for Precision Agriculture (AP) purposes which require the monitoring of spatial variability regarding plant growth, especially to assess variation at plant level over time in perennial crops, such as banana plantations. However, spatial misalignments between orthomosaics from different dates requires post-processing image matching, i.e., co-registration, to ensure reliable spatiotemporal variability analysis. This study proposes a method for the co-registration of RGB U... B.R. Costa, J.P. Molin, B. Barreto

84. Combining Orbital and Proximal Sensing to Map Management Zones in Precision Viticulture: A Spatiotemporal Analysis

Precision agriculture stands out by mapping the spatiotemporal variability of vineyards to understand the interdependence between causes and effects throughout production cycles. Vegetative vigor, which can be estimated using vegetation indices calculated from proximal and orbital sensors, is a fundamental parameter for indicating this variability and assisting in the definition of potential, time-constant management zones. The objective of this study was to evaluate and compare the use of pr... E.A. Speranza, J.R. Da Silva, L.R. Correa, L.H. Bassoi

85. Combining YOLOv9 and Fuzzy Inference System to Improve the Precision of Weed Recognition Systems in Soybean Crops Using UAV Imagery

Weeds are a problem in crops because they compete with crops for nutrients, sunlight, and water, hindering their full development. To control these plants, herbicides are usually applied throughout the field. Therefore, to optimize the application process, many researchers have been working on automatic weed recognition systems based on artificial intelligence techniques for field imaging, enabling the localized application of herbicides. To this end, the YOLO (You Only Look Once) object dete... M. Tosin, I. Scheeren, C. Markus

86. Comparative Analysis of Technical and Economic Feasibility of Pepper (Capsicum Annuum) Cultivation Under Artificial Lighting Vs. Protected Environment

Contemporary agriculture is undergoing a technological disruption marked by the transition from natural biological dependence to an industrial precision science. At the heart of this shift, the cultivation of pepper (Capsicum annuum) in Controlled Environment Agriculture (CEA) settings utilizes artificial lighting to transform electromagnetic radiation into a programmable input, eliminating solar variability. This study conducted a comparative technical and economic feasibility analysis betwe... L. De Arruda Viana, A. Luiz De Carvalho, R. Ivo Soares Avelar

87. Comparative Analysis of YOLOv3–YOLOv12 Architectures for Automatic Oil Palm Detection in Agricultural Monitoring

Oil palm (Elaeis guineensis) is considered the most productive oilseed crop worldwide, and Brazil holds one of the greatest global potentials for palm oil production. Efficient monitoring of cultivated areas is therefore essential for proper crop management, enabling the detection of planting gaps, yield estimation, and decision-making support. In this context, computer vision techniques based on deep learning models, particularly those from the YOLO (You Only Look Once) family, have... M.C. Arnosti, A. Felipe Dos Santos, T. Costa Barboza, L.S. Souza Pinto, E. Amaral, G. Lacerda Da Silveira, G. Valdes Fernandez

88. Comparative Assessment of Proximal Sensing and UAV Multispectral Data for Coffee Vigor Analysis

The assessment of vegetative vigor through spectral indices, particularly the Normalized Difference Vegetation Index (NDVI), is widely adopted in precision agriculture as a rapid, indirect, and non-destructive method for evaluating plant physiological status. A broad range of sensing technologies has been employed for this purpose, including proximal sensors and multispectral imaging systems mounted on remotely piloted aircraft (RPAs). In this context, the objective of this study was to evalu... E. Zavala, G. , M.D. Oliveira, H. Khalid, P.

89. Comparative Evaluation of Combined and Task Specific Detectors for Pomegranate Yield and Fruit Loss Detection

Fruit cracking and drop represent major sources of yield loss in pomegranate orchards; however, existing vision-based yield estimation methods focus on counting healthy fruit and do not usually capture losses occurring on-tree and on the orchard floor, thereby constraining their operational relevance. This study evaluates detection strategies for simultaneous yield and loss quantification, with a specific comparison between combined multi class models and task specific single class models.... Y. Tenenboim, Y. Edan, I. Ginzberg, T. Paz Kagan

90. Comparative Evaluation of Ground Point Classifiers in LiDAR Point Clouds for DEM Generation in Pasture Areas

The classification of ground points in LiDAR point clouds is an essential step for generating reliable Digital Terrain Models (DTMs), particularly in livestock production systems based on pastures. Despite methodological advances in forested and urban environments, studies specifically addressing ground classification in pasture areas remain limited, where the proximity between the forage canopy and the ground surface makes altimetric distinction between classes challenging. The heterogeneous...

91. Comparing Traditional Methods and Digital Platforms for Delineating Management Zones: A Study of Efficiency and Accuracy

Digital platforms have emerged as user-friendly tools to support management zone delineation and field monitoring in precision agriculture. However, the algorithms and methods embedded in these platforms may overlook agronomic and operational constraints, limiting their effectiveness in decision-making. This study evaluated the performance of three commercial digital platforms for management zone delineation and compared them with a reference protocol and an... T. Costa Barboza, H. Oldoni, F.D. Inácio, L.R. Amaral, A. Felipe Dos Santos

92. Comparing UAV-based Multispectral Indices with Thermal and Energy Balance Models for Barley Yield Components Estimation

Accurate estimation of barley yield components is essential for improving crop management and breeding strategies under contrasting water regimes. This study evaluates the potential of integrating unmanned aerial vehicle (UAV)-based multispectral and thermal imagery with energy balance modeling to predict grain yield (GY), thousand kernel weight (TKW), and grain filling period (GFP). A recombinant inbred line (RIL) population derived from SBCC073 × Cierzo was grown under irrigated and r... D. Gomez-candon, A. Cabeza, D. Villegas, A.M. Casas, E. Igartua

93. Comparison Between Conventional and Aerial Spraying Using Remotely Piloted Aircraft in the Control of the Coffee Leaf Miner

A traça-do-café (Leucoptera coffeella) é uma das principais influências que influenciam o cultivo do café, impactando diretamente a produtividade da cultura. Seu controle pode ser desafiador em algumas áreas devido às condições do terreno e à escassez de mão de obra. Este estudo teve como objetivo comparar a eficiência agronômica e a dinâmica de controle do traçado-do-café utilizando do... W. Batista Da Silva , A. Santos, A. Felipe Dos Santos, T. Costa Barboza, O.P. Costa, G.L. Silveira, R.Q. Alves

94. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and Generalizability

Cotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar

95. Comparison of Orbital and UAV Remote Sensing for Coffee Crop Monitoring in Mountainous Terrain

Monitoring coffee crops is an important step for the success of production systems. Recently, manual field inspections have been replaced by automated techniques aimed at improving spatial coverage and reducing costs. One such technique is remote sensing, which can be performed using both orbital platforms and Unmanned Aerial Vehicles (UAVs). However, coffee cultivation presents significant imaging challenges due to plant spacing, where wide row spacing results in greater spectral variability... I. Araújo Barbosa, M.H. Pereira, D. Queiroz, A.L. Coelho, D. Sárvio Valente, M.C. Moreira

96. Comparison of Real and Simulated GSD in the Estimation of Canopy Height in Maize Using RPA

High-throughput phenotyping using remotely piloted aircraft (RPA) has become a strategic tool in precision agriculture, enabling rapid and non-destructive estimation of crop traits such as canopy height. Ground Sampling Distance (GSD) is a critical flight parameter in this process. Resolutions derived from smaller GSD values improve accuracy but increase flight time, image volume, and processing cost, whereas the opposite reduces these demands at a possible cost to accuracy. Simulating coarse... C. Ragalzi, M.J. Lima, L. Felipe, N. Guimarães, M.F. Santos

97. Consistency of Three Vegetation Indices from Suborbital and Proximal Sensing in Precision Viticulture

The integration of proximal and suborbital sensing platforms can expand the practice of precision viticulture. However, the consistency of vegetation indices (VIs) derived from different sensors remains a critical issue. This study quantified the agreement between VIs obtained by proximal and suborbital sensing using complementary metrics of association, error, and agreement. The research was conducted in a ‘Syrah’ vineyard in Ribeirão Preto, state of São Paulo, Braz... L.H. Bassoi, L.A. Jorge, A. Pereira, I. Oliveira Junior

98. Consolidation of Factors Influencing the Design of an Electrically Driven Seed Metering and Delivery Mechanism for High-speed Operation.

The development of innovative products to address agricultural challenges has become essential for more efficient operations, resulting in increased productivity. In this context, the parameters of Design Influence Factors (DIFs) define the technical and functional guidelines of the machinery, ensuring that the final product fully meets the demands defined throughout its life cycle. This study proposes the consolidation of DIFs applied to the development of an electrically driven seed meterin... J. Santos, V. Kaster Marini

99. Correcting LiDAR-based Plant Height Estimation Errors in Dense Cotton Canopies

Cotton is a perennial plant grown as an annual crop and, if not properly managed, excessive vegetative growth may reduce yield, making the use of plant growth regulators (PGR) essential. In precision agriculture, spatial representation of PGR requirements depends on plant height measurements, which are typically labor-intensive. LiDAR sensors mounted on drones have been widely used to estimate plant height. However, under certain conditions, cotton plants can become highly vigorous, resulting... P. Zolin, L. Peranzoni Deponti, L. Bastos, L.R. Amaral

100. Cost-Optimized NPK Fertilizer Recommendation Using Linear Programming and Mixed Integer Linear Programming for Precision Agriculture

Fertilizer management is one of the most cost-intensive stages of crop production, yet many small and medium-sized brazillian farmers still rely on empirical experience or generalized guidelines to make fertilization decisions. This often results in nutrient imbalances, economic inefficiency, and unnecessary environmental impact. While precision agriculture technologies such as variable-rate application systems and georeferenced soil sampling have advanced considerably, accessible computation... M. Bodanese, R. Sobjak, C.L. Bazzi, K. Schenatto, A. Sandmann

101. Cotton Yield Response to Seed Density in Contrasting Productive Potential Zones

ABSTRACT: Brazilian cotton farming has high economic and agricultural importance. However, it faces challenges related to management practices that consider the spatial variability of productivity across different yield potential zones within a field. In this sense, precision agriculture tools have great potential to optimize and rationalize input use in areas with spatial variability in productive potential, especially through the use of variable rate seeding. Nevertheless, ... G. , C. Bredemeier, R. , D.

102. Critical Competencies for Digital and Precision Agriculture: Evidence from Literature and Stakeholder Perspectives

The transition toward digital and precision agriculture is transforming agricultural production through the integration of artificial intelligence, sensor technologies, geospatial systems, robotics, automation, and advanced data analytics. As agricultural systems become increasingly data-intensive and technology-driven, identifying the competencies required for effective implementation has become essential. Although the literature discusses skill requirements in Agriculture 4.0 contexts, limi... J. Mattar, L. Cuque

103. Crop Production and Nutrient Management with Strip-tillage in the Northern Great Plains

Conventional tillage is still the dominant tillage practice in the eastern third of South Dakota (USA). Producers are often facing wind and water soil erosion in this part of the state. Strip-tillage can offer an alternative production option that could lower erosion potential between cash crops, while maintaining the yield potential. In addition strip-tillage allows a same-pass nutrient placement. The research objectives are to compare tillage and fertilizer placement methods in a corn (... P. Kovacs, C. Tesch, N. Passone

104. Cross-Season Transfer Learning for Prawn Morphometric Estimation Using YOLOv11-Pose

The problem of maintaining accurate computer vision models in dynamic aquaculture pond environments is increasingly important as real world imaging conditions vary over time. Even in controlled indoor ponds, factors such as water turbidity, lighting angle, background reflections, and camera setup can change between monitoring sessions or seasons. These variations introduce domain shifts that can significantly degrade the performance of deep learning models trained under controlled conditions.... T. Carmon, E. Aflalo , A. Sagi, Y. Edan

105. Current Scenario of PA Service Provision in Brazil

... F. Martins

106. Current Scenario of Precision and Digital Agriculture in Europe

... D. Cammarano

107. Current Scenario of Precision and Digital Agriculture in the USA

... S. Virk, B.V. Ortiz

108. Data Analytics in Precision Agriculture: Statistical Modelling and Machine Learning

... M. Córdoba, P. Paccioretti

109. Data Governance Platform for Precision Agriculture: Enhancing Traceability and Sustainability

Precision Agriculture (PA) is one of the enablers of data-driven agriculture. Digital Agriculture (DA) tools are increasingly vital in driving the adoption of PA techniques across small, medium, and large-scale farming operations. These technologies, including the Internet of Things (IoT), sensors, drones, satellite imagery, Artificial Intelligence (AI), and Big Data, work synergistically to capture detailed information on soil conditions, plant health, climate, and machinery performance. Thi... E.A. Speranza, R.Y. Inamasu, L.A. Romani, J. Naime, R. Sobjak, I. Vacari, C.L. Bazzi, S. Shibusawa

110. Deep Learning in Seed Vigor Assessment: Analysis of U-Net Family Architectures for Soybean Seedling Segmentation

In response to the growing challenges faced by agricultural production, increasing productivity in already cultivated areas has become essential to ensure global food security. In this context, the use of high-vigor seeds is crucial to achieving higher crop yields. However, traditional vigor assessment methods are time-consuming, often manual, and dependent on specialized labor, which drives the search for automated solutions based on Computer Vision and Deep Learning techniques. Several stud... J. Martins Neto, P. Dos Santos E Silva, E. Freitas, D. G. Gomes, H.F. Abud

111. Deep Learning Models Applied to Drone Imagery for Counting, Biometry, and Carbon Stock Estimation in Large-scale Macaw Palm (Acrocomia Aculeata) Plantations

Macaw palm is a native Brazilian species with significant productive potential, emerging as a premier candidate for the sustainable replacement of oil palm and as a strategic feedstock for sustainable aviation fuel (SAF) and carbon credit markets. However, as the crop is still in the process of domestication and commercial expansion, there is an urgent need for efficient monitoring technologies that enable the identification of superior individuals and the rigorous auditing of carbon stocks a... P.M. De Sousa, V.A. Galvan, J. Souza, R. . De Oliveira , L.D. Corrêdo, L.D. Pimentel, B.C. Albuquerque

112. Deep Learning-based Anomaly Detection System for Rice Crop Health Monitoring

Global food security relies heavily on the stable production of rice (Oryza sativa L.), yet cultivation remains vulnerable to various phytosanitary anomalies, including foliar diseases like Pyricularia and Rhynchosporium, scald, and abiotic stressors such as herbicide damage. Traditional agronomic management relies on visual scouting, which is inherently subjective, labor-intensive, and often leads to delayed interventions. This study proposes an automated, high-throughput solution for real-t... F.R. Jimenez Lopez, A. Jimenez, D.Y. Garcia Ramirez

113. Definition of Flight Height for Image Monitoring of Pupunha Palm Cultivation

The use of Unmanned Aerial Vehicles (UAVs) associated with computer vision has expanded the applications of precision agriculture, especially in perennial crops that require detailed spatial monitoring. In the peach palm tree, the automated detection of clumps from aerial images is an efficient alternative to traditional methods, contributing to management and production estimates. Thus, the objective of this study was to evaluate the influence of flight height on the performance of ... D.A. Botta De Siqueira, J.P. Molin, B.B. Barreto, M.L. Silva, P.R. Fiorio

114. Delimitation of Management Zones in Agroforestry Coffee Systems

The search for more sustainable agricultural production systems is a common goal for both agroforestry systems and precision agriculture. Integrating precision agriculture technologies into coffee cultivation within agroforestry systems has the potential to make these systems more efficient, productive, and sustainable. Thus, this study aims to test the effect of different variables on the delimitation of management zones (MZs) in agroforestry systems. The study was conducted on two coffee-pr... W. Silva Dos Santos, D. Queiroz, F.C. Pinto, A.L. Coelho, B.N. Vasconcelos

115. Delineation of Agroecological Zones for Rainfed Summer Crops in Eastern Uruguay

The delineation of agroecological zones is a key step for understanding spatial variability in crop performance and for supporting the assessment and transferability of agricultural technologies. Even within relatively small regions, strong gradients in climate and soil properties can lead to substantial differences in crop yield potential and stability, particularly in rainfed systems. This study aims to delineate agroecological zones for rainfed summer crops in eastern Uruguay using an inte... D. Gaso, I. Macedo

116. Delineation of Management Zones for the Adoption of Precision and Digital Agriculture in Steep-Sloped Arabica Coffee Production Areas

Coffea arabica production in Brazil, particularly in regions of São Paulo and Minas Gerais, occurs in environments with a high diversity of climates, altitudes, and soils. The municipality of Caconde (SP) stands out with approximately 11,000 hectares of coffee, predominantly on small properties with altitudes above 800 meters and steep slopes. These characteristics are conducive to the production of high-quality, value-added coffees. Optimizing the use of natural resources and agricult... E.A. Speranza, C.R. Grego, T. Santos, G.C. Rodrigues, R.Y. Inamasu

117. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar Beets

The global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-B... A. Tabbassi, S. Henkler, A. Zakhary, K. Rother

118. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar Beets

The global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-B... A. Tabbassi, S. Henkler

119. Detection of Aggressive Group Behavior of Laying Hens in Free Henhouses

Harmful social behaviors among laying hens, such as feather pecking and aggressive chasing, pose a significant challenge to animal welfare and productivity in cage-free poultry systems. While cage-free housing allows hens to express natural behaviors, it also increases the risk of injurious interactions that can lead to stress, injury, and economic losses. Existing mitigation strategies, including environmental enrichment and housing design improvements, reduce but do not eliminate harmful be... Y. Heller, S. Druyan, A. Godo, V. Bloch

120. Detection of Banana Bunches and Peduncles in the Prata Catarina Cultivar Using Faster R-CNN With Transfer Learning

Banana is one of the most produced and consumed fruits worldwide, being strategic for precision agriculture, especially in applications aimed at intelligent management and automated harvesting. Its economic and social relevance in tropical countries reinforces the need for technological solutions that increase productive efficiency and reduce losses in the field. In this context, the automatic detection of bunches and stalks in a natural environment represents a relevant challenge due to occl... Y. Costa G. Da Silva, E. Freitas, P.S. Costa, D.V. Beserra, D.G. Gomes

121. Detection of Chlorophyll A and B in Maize Using Visible Reflectance under Conventional and Variable Rate Nitrogen Management

Plant-environmental interactions regulate physiological aspects that determine crop yield. However, measuring physiological parameters in-field remains a challenge, as sampling often requires time-consuming laboratory analysis. Therefore, this study analyzed the influence of reflectance using a handheld sensor in detect chlorophyll A and B in different nitrogen application strategies before and after sidedressing. The experiment was conducted at a commercial farm in Campo do Meio, Minas Gerai... T. Costa Barboza, O.P. Da Costa, R.F. Queiroz, L. Lacerda, A. Felipe Dos Santos, S.G. Silva

122. Detection of latrine areas in equine paddocks using drones and computer vision

Equines can exhibit behaviors that are harmful to the soil, such as spatial segregation, which is caused by their selective grazing pattern. This species may choose its feeding areas based on vegetation structural characteristics, such as forage density, leaf availability, and stage of maturity (which are perceived through their tactile receptors). When present daily, this natural behavior can impair soil health, as spatial segregation within paddocks intensifies and latrine (dung) areas form...

123. Detection of Maize Foliar Diseases Using AI Optimized for Deployment on Edge Devices

Maize is a strategic crop for both regional and global food security. Its productivity is significantly affected by several foliar diseases, among which—common rust, gray leaf spot, and blight—are some of the most prevalent and damaging. These pathologies can cause substantial yield losses if not detected and treated in a timely manner, making early diagnosis a fundamental factor to ensure healthy and sustainable crop development. However, traditional diagnostic methods based on m... O.J. González Zarate, L. Macea Zabaleta, N. Castillo Ojeda, A.F. Flórez Olivera, T.A. Rodolfo, C. Gonzalez Aguilera

124. Detection of Plants with Xylella fastidiosa in Olive Orchards Using Aerial Multispectral, Thermal Imagery and Machine Learning

Early detection of Xylella fastidiosa in olive orchards remains a significant phytosanitary challenge due to the difficulty of identifying infected plants during the initial symptom development phase. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable approach for disease monitoring at field scale. This study evaluated the potential of spectral indices derived from multispectral and thermal imagery for classifying the occurrence of... F. Silva, M. Antônio, G. Koch, P.A. Moura, C.C. Santana

125. Detection of Sticky Disease (PMeV) and Papaya Ringspot (PRSV-P) in Papaya Plants (Carica papaya) Using Optical Sensors

The papaya plant (Carica papaya L.) is one of the main tropical fruit crops cultivated in Brazil, with significant economic and social importance, especially in the state of Espírito Santo. Despite its high productive potential, the crop has been severely affected by viral diseases, notably papaya ringspot, caused by Papaya ringspot virus type P (PRSV-P), and sticky disease, associated with the viral complex PMeV and PMeV2. Given this scenario, the present study aimed to evaluate the p... D.C. Gonçalves, S. . Hatum, M. Sousa Vieira , D. . Júnior, W. . Machado, A. . Nunes, N. . Corrêa Dalvi, R.L. Polastreli

126. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 Imagery

Weed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this st... R.P. Amaro, F. Amstalden, C. Berro Filho, D.G. Duft

127. Determinants of the Intensity of Digital Precision Technology Adoption in Brazilian Feedlots

Precision livestock farming has gained prominence as a tool to enhance managerial control and reduce risk in intensive production systems. In the case of Brazilian beef cattle feedlots, characterized by high price volatility, tight margins, and increasing pressure for environmental performance, the adoption of digital technologies represents a relevant strategy to improve decision-making processes. Unlike studies that focus solely on binary adoption (adopt/non-adopt), understanding adoption i... G. , M.J. Carrer, M. , L.C. David, H.M. Souza Filho, E. Bonjour

128. Determination And Calculation Of Eucalyptus Biomass From Lidar Sensor Data And Projection Of Available Biomass

Eucalyptus (Eucalyptus spp.) is one of the most widely cultivated forest species in Brazil and worldwide. Eucalyptus biomass is organic matter derived from the eucalyptus tree which can be used as a renewable energy source. In recent years, advances in remote sensing technologies have made it possible to estimate forest biomass more accurately and non-destructively, with the use of LiDAR (Light Detection and Ranging) sensors being particularly noteworthy. This system emits r... J.P. Da Silva, G.A. Araujo, L. Carvalho, J.P. Verçosa, A.C. Tavares

129. Development and Evaluation of a Novel Seeding Metering System for Mechanic Seeder Toward Precision Agriculture

Recent progress in precision and digital agriculture has increasingly relied on the integration of computational modeling, sensor-based analysis, and data-driven design to improve agricultural machinery performance. Seed metering systems are central to this progress, as they regulate seed delivery for both uniform crop establishment and variable-rate seeding applications. In conventional agricultural systems, where field conditions are assumed to be relatively homogeneous, uniform seed spacin... E. Jotautienė, D. Karayel, H. Yilmaz, A. Grigas

130. Development and Field Validation of a Mid-Infrared Proximal Sensing System for In-Season, On-Vine Monitoring of Grape Composition

Accurate in-season monitoring of grape composition is important for precision agriculture, as it supports timely decisions on crop management and harvest scheduling. In practice, however, commonly used approaches for assessing internal quality—such as refractometry and near-infrared (NIR) spectroscopy—are often applied to harvested samples, which limits their use for truly non-destructive measurements on developing fruit in the field. We have been developing a novel mid-... H. Furukawa

131. Development and Field Validation of a Scalable UAV-Based Framework for Automated Cattle Counting and Herd Management in Extensive Production Systems

Brazil holds the largest commercial cattle herd in the world, with more than 230 million head, representing approximately 20% of the global population. In this context, technologies capable of optimizing herd monitoring are strategic for increasing production efficiency, reducing operational costs, and promoting sustainability in livestock systems. Among these technologies, computer vision–based systems have emerged as a promising alternative for automated animal detection and counting ... F. H. S. Sousa , T. S. Maciel, M. M. Dos Reis, R.D. Santos, A. M. Santos, A. M. S. De Souza, A. K. F. Veras, G. G. Ferreira, M. P. M. Nunes, M. C. R. Seruffo, L. C. C. Daher, A. G.m. Silva

132. Development and Field Validation of SMART-C: A Geostatistics and PCA-Based Decision Framework for Site-Specific Cocoa Management in the Brazilian Amazon

Cocoa production plays a major socioeconomic role in Pará State, Brazil’s largest producing region, with annual output exceeding 140 thousand tons. Although Brazil ranks among the world’s leading cocoa producers, most production systems are still managed using field-average approaches that disregard within-field spatial variability of soil attributes and crop performance. This limitation restricts input efficiency and long-term system sustainability in perennial tropical sy...

133. Development and Validation of a Low-Cost IoT-Based Weather Station Using LoRa Communication for Precision Agriculture

Access to accurate local meteorological data remains a critical bottleneck for precision agriculture adoption among small and medium-scale Brazilian farmers. Commercial weather stations cost between R$ 15,000 and R$ 50,000, while public networks such as INMET operate with average inter-station spacing of 30–50 km, insufficient to capture the microclimate variability that drives field-scale irrigation and crop management decisions. This study presents the development, field validation, a... A.L. Carvalho, C.C. Santana, R. Avelar, F.D. Silva, F. Soares

134. Development of a Label-free Electrochemical Biosensor for Detection of Infectious Hepatitis a Virus

One of the leading causes of foodborne viral illnesses in the world is Hepatitis A Virus, which is frequently involved in causing outbreaks linked to contaminated produce and shellfish (e.g., green onions and berries) due to contamination during cultivation, processing, or handling. HAV is highly stable in environment capable of remaining infectious on food matrices and in water for extended periods. Humans get infected primarily via the fecal-oral ro... D. Kaur, R.P. Ramasamy, M. Esseili

135. Development of a LoRaWAN Network for Remote Sensing in Precision Agriculture

The modernization of agriculture through Agriculture 4.0 requires the use of advanced sensors and wireless communication networks for the precise monitoring of environmental variables and process optimization. However, the implementation of these Internet of Things (IoT) technologies in rural areas frequently faces the challenge of limited infrastructure over large territorial expanses. In this scenario, Low-Power Wide-Area Networks (LPWAN), specifically the LoRaWAN protocol, stand out for th... M. Hermes, M. Albuquerque, A. Andreoli, C. , C. Chaves

136. Development of a Predictive Machine Learning Model for Pasture Biomass Using Satellite Vegetation Indices and Climate Data in Spanish Dehesa Systems

Extensive livestock systems are fundamental to the ecological, economic, and cultural sustainability of Mediterranean agroecosystems such as the Spanish dehesa. These silvopastoral landscapes support biodiversity, prevent land abandonment, and sustain rural livelihoods, but their productivity is highly dependent on pasture availability. Efficient management therefore requires accurate and timely information on pasture biomass, which is strongly influenced by climatic variability, soil propert... C. Ferraz, A. Tamayo López, A. Do Vale Dondo

137. Development of a System for Intelligent Plant Monitoring and Cultivation

Cultivation in protected environments and indoor systems requires continuous monitoring. Labor shortages and delays in management decisions compromise productivity, uniformity, and efficiency. Assessments of plant stand, vegetative vigor, nutritional status, and the incidence of pests and diseases still rely on visual inspections conducted over limited periods, reducing diagnostic accuracy and response time. Although automation technologies are advancing in horticultural production, available... R. Avelar, F.O. Dos Reis Silva, A.L. Carvalho, F.M. Alves Soares, C.C. Santana

138. Development of an IoT Platform for Soil and Climate Monitoring in Irrigated Fruit Production in the Semi-Arid Region of Pernambuco

Irrigated fruit production in the São Francisco hinterland, led by the Petrolina production hub, reached US$ 294 million in exports in 2023, consolidating mango and grape crops as strategic pillars of Pernambuco’s economy and of the Brazilian semi-arid region. This production system is dependent on irrigation due to irregular rainfall distribution, high evaporative demand, and recurrent drought conditions. Despite its international competitiveness and technological advances in ir... A. Fonseca, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, A.D. Ribeiro

139. Development of Predictive Models for Determining Organic Carbon and Clay Content in Soils under Irrigated Fruit Production in the Brazilian Semi-Arid Region

The agricultural sector plays a pivotal role in both greenhouse gas emissions and climate change mitigation through soil carbon sequestration. Total organic carbon (TOC) and soil texture - particularly clay content - are key indicators of this dynamic, as they influence organic matter stabilization, water retention, and soil structural quality. In semi-arid regions, where edaphoclimatic conditions and water scarcity constrain agricultural production, the integrated assessment of these at... A.L. Santos, A.A. Linhares, P.C. Barbosa , W.C. Lopes, M.D. Reis, K.D. Rocha, D.G. De Oliveira, M.S. Rodrigues, D.D. Costa

140. Digital Agriculture in Dairy Farming: Connectivity Diagnosis and Barriers to Technology Adoption in an Agrotechnological District

The integration of digital tools and communication systems has underpinned a profound transformation in global production chains, aiming to optimize farm operations through technological innovation. Within this context, the present study forms part of the Semear Digital project, coordinated by Embrapa, and is grounded in the premise that digital inclusion constitutes an indispensable strategy for the sustainability and competitiveness of contemporary dairy farming. The primary object... M.D. Melo, C.M. Paiva, A.L. Oliveira, F.N. Maciel, G.C. Siqueira , M.R. Borges , G.S. Furtado, P.M. Leme

141. Digital Agriculture in Decision-Making for Sustainable Disease Management in Soybean Crops

Soybean (Glycine max L.) stands out as one of the main crops of agronomic interest, widely used in human and animal nutrition due to its high protein content and diversity of derivatives. Soybean crop productivity is strongly influenced by meteorological conditions, adopted management practices, and the incidence of pathogens, which can significantly reduce the plant’s photosynthetically active area, directly impacting final yield. In this context, the present study aimed to ev... E. Rolim Farias Da Silva, L. Lüdtke, G. Ductra Bortolotti, I. Maldaner, L. , J. Sgarbossa, L. Silveira Pavão, A. Müllich

142. Digital Livestock Management Solution for Cattle Identification, Traceability, and Real-time Monitoring

Brazil is a global player in the beef industry with the world's largest commercial bovine herd, exceeding 230 million head, and leads the international market, accounting for approximately 25% of the global beef trade, reaching over 150 international markets. The combination of edaphoclimatic diversity, high-performance genetics, rigorous sanitary protocols, and the adoption of technological framework for tropical livestock accounts for decoupling of Brazilian ranching from extensive, low... A. Bernardi, A.R. Garcia, E.S. Guimarães, F. Tonato, S.R. Medeiros, W. Barioni Jr., J.B. Portugal, T.C. Alves, W.P. Cavalcante, M. Serão Filho, C. Gaioli Jr

143. Digital Terrain Modeling and Topographic Smoothing for Levee Optimization in Irrigated Rice Areas Using AgroCAD® and T3rra Cutta©

The systematization of lowland areas intended for irrigated rice production requires precise topographic planning to ensure efficiency in water management and operational performance of agricultural activities. Traditional land leveling methods are often based on empirical approaches, which may result in excessive soil movement and inefficient levee configurations. Advances in Precision Agriculture have enabled the integration of high-precision GNSS positioning with digital terrain modeling t... A. Mullich, I. Maldaner, L. , L. Wendt, J. Turchiello, M. Noal Santarem, M.L. Auzani Biscaino

144. Digital Transformation and Efficiency Gains in Intensive Livestock Systems: Evidence from Brazilian Feedlots

Precision livestock farming has emerged as central strategies to enhance productive efficiency, reduce waste, and improve the sustainability of agricultural systems. In beef cattle feedlots, digital technologies such feeding automation sensors is particularly relevant. The technology reduces feed waste, improves the planning of input purchases and cost control, and reduces the need for manual labor for weighing and distributing feed, allowing the team to focus on strategic activities. In the ... L.C. David, M.J. Carrer, M. , H.M. Souza Filho

145. Do Precision and Climate Concerns Shape Fertilizer Usage Proportions? Evidence from Denmark

Farmers are constantly facing pressure to enhance crop productivity while minimizing environmental and climate impacts through efficient input management. In Denmark, because of concerns over nitrogen leaching, greenhouse gas emissions, and water quality degradation, there are tight regulations to fertilizer use. This makes precision farming technologies a key to the efficient management of nutrients through site-specific input application based on crop and soil variability. However, adoption... N. Yenibehit, S.M. Pedersen, T.W. Tamirat

146. Drones as a Tool for Digital Agriculture in Soil and Water Management and Conservation

The use of drones in agriculture has evolved rapidly; however, it is still underutilized in one of the most strategic areas of agricultural production: conservation planning of the terrain. This short course proposes a practical and applied approach on how to use imaging drones for planialtimetric digitalization of agricultural areas, focusing on soil and water management and conservation. Based on the generation of digital surface and terrain models (DSM/DTM), techniques will be de... A. Müllich

147. E-Demofarm as an Integrated Framework for Education, Training and Extensión in Precision Viticulture

The concept of the demonstration farm has long been central to agricultural education and technology transfer, providing tangible spaces where innovation meets practice. However, traditional approaches remain constrained by geographic and accessibility limitations and the limited integration of modern digital tools, hindering the large-scale dissemination of innovation and practical training. To address these challenges, the e-Demofarm is designed and investigated within the VTskill project, ... M. Pérez-ruiz, T. Scali, A. Michailidis, D. Sarri, A.M. Mouazen

148. Early Detection of Soybean Pest Infestations Using Leaf-Level Reflectance Spectroradiometry and Machine Learning

The agricultural sector plays a central role in sustaining global food production, energy supply, and economic development. However, population growth, climate change, resource scarcity, and increasing sustainability demands have intensified production challenges. Pest and disease outbreaks are major contributors to crop losses worldwide, underscoring the urgent need for reliable methods capable of enabling early detection and timely intervention. In this context, leaf-level spectroradiometry... M. Lima, J.C. Felipe, E.J. Ferreira, L.A. Jorge, L. Zhao

149. Early Forecasting of Maize Lodging Risk Through Multi-period and Multi-source Data Integration

Lodging is a critical constraint on global maize (Zea mays L.) productivity, primarily through detrimental effects on both grain yield and quality. However, reliable methods to predict maize lodging risk early in the growing season are lacking, which hinders timely implementation of effective agronomic management interventions to increase crop lodging resistance and reduce corresponding yield losses. This work aimed to develop a feasible early season maize lodging risk prediction met... L. Dong, Y. Miao, X. Wang, P. Berry, D. Hatley, K. Kusnierek

150. Early Yield Estimation in Hass Avocado Using Ecophysiological Variables and Machine Learning

This study evaluated the ability of machine learning models to estimate yield in mature Hass avocado trees (>5 years), using ecophysiological variables measured with MultispeQ v2.0 (RIDES 2.1 protocol) and electrical capacitance (1 Hz). The study was conducted at Pan de Azúcar farm (Villahermosa, Tolima, Colombia; 1,565 m a.s.l., Andisols) on 60 trees, with data collected across four phenological stages (fruit development, fruit maturation, leaf and shoot growth, and pre-flowering) ... D. Rayo Álvarez, P.J. Murillo Sandoval, A.E. Darghan Contreras, D.F. Conejo Rodriguez

151. Economic and Ecological Performance and Farm-level Adoption of Market-available Tools for Variable-rate Nitrogen Management in a Region of Small-to-medium-scale Agriculture

The proposed contribution combines the results of extensive multi-year field trials on variable rate-nitrogen fertilization (VRN) of winter wheat with the results of a series of farmer surveys, and findings drawn from a government investment subsidy program. All three data sources (field trials, surveys, investment subsidy program) cover roughly the same period and agricultural area. The field trials were conducted from 2023 to 2025, the surveys in 2020, 2022, and 2025, and data on the invest... M. Gandorfer, B. Vinzent, J. Pfrombeck, J. Garnitz, F. Maidl

152. Economic Assessment of Soil Sampling Densities for Variable-Rate Fertilizer Prescription

Soil sampling and laboratory analysis represent a substantial share of operational costs in precision agriculture, making sampling density a crucial management parameter. Sampling density defines the resolution at which soil spatial variability is captured and directly influences the accuracy of spatial interpolation and fertilizer prescription maps. Due to high operational costs, reduced sampling densities are commonly adopted in practice, despite their known effects on map quality. This stu... L. Delgado Bejarano, B. , A. Novaes Da Silva, L.R. Amaral

153. Economic performance of Robotic Applications in Field Crop Farming: Examples from Four European Case Studies

This paper presents cost benefit analysis of robotic applications in field crop farming based on case studies from an EU project called ROBS4CROPS with pilot cases situated in Loire valley in France, Corinth in Greece, Catalonia in Spain and Oldambt in the Netherlands. The cases form France and The Netherlands focus on mechanical weeding in vineyards and sugar beet, respectively. The ones in Spain and Greece deal with robotic spraying of crop protection chemicals in apple orchards and table g... T.W. Tamirat, S.M. Pedersen

154. ECOROBOTIX - Leading the Plant-by-Plant Spraying Revolution in Global Agriculture

... J.M. Marchetti

155. Edge AI–Driven Soil Sensing and Fertilization Prediction for Solanum betaceum

The growing demand for data-driven fertilization strategies in high-value perennial crops has fostered the development of intelligent systems capable of supporting decision-making directly in the field. In the case of tree tomato (Solanum betaceum), fertilization is commonly performed based on fixed schedules or empirical criteria, which often fail to account for soil dynamics and nutrient variability. This study investigates the feasibility of an embedded artificial intelligence system that ...

156. Effect of Post-processing on the Performance of Clustering Algorithms for Delineating Management Zones for Precision Soil Sampling

Soil nutrient variability directly influences crop yield; therefore, site-specific management requires maps that can depict variability within the fields. Here, management zone (MZ) approaches have been used, typically derived from clustering analyses applied to low-cost environmental variables.  There is still no consensus on zone delineation strategies that maximize the reduction of soil variability, nor on the relative performance of clustering algorithms. Moreover, post-processing of... D.D. Melo, L.R. Amaral, L. Bastos, S. Virk

157. Effect of Terrain Slope Obtained by LiDAR on Operational Performance in Semi-mechanized Coffee Transplanting with Autopilot.

The application of precision agriculture techniques has become an important tool in surveying coffee plantations, allowing for the rapid assessment of slope profiles in these areas and indicating the possibility of mechanizing the plots. Therefore, there is a need to work with quality in semi-mechanized transplanting operations using autopilot to optimize future processes related to coffee cultivation. The objective of this study was to determine the efficiency of use and mechanical availabil... R. , G. , R.D. Faria, L.S. Silva, M.D. Oliveira, E.H. Zavala

158. Efficiency of the Production System for Pre-sprouted Sugarcane Seedlings Produced in an Indoor Environment.

Objectives To evaluate the production efficiency of pre-sprouted sugarcane seedlings (PSS), produced indoors with artificial lighting and controlled atmosphere, in a reduced 30-day cycle, compared to seedlings produced by the traditional method, in 50- and 70-day cycles. Methods and Procedures The experimental trials were conducted at the IAC Sugarcane Center, located in Ribeirão Preto (SP), in two stages. Stage 1: seedlin... E. Prudenciatto Clemente, E. Prudenciatto Clemente

159. Embrapii: What Can We Learn About Precision Agriculture and Environmental Sustainability After Investments of US$100 Million+ in Agricultural Industrial Innovation in Brazil?

Innovation policies have increasingly targeted digital and sustainable transformation, yet systematic assessments of large-scale public-private investments in precision agriculture innovation remain scarce, particularly in tropical contexts. This article analyzes the experience of Embrapii (Brazilian Agency for Research and Industrial Innovation) after fostering more than US$100 million in investments in over 600 research, development, and innovation (RD&I) projects applied to agriculture... J. Videira Menezes, E.M. Dias, M.L. Rebello Pinho Dias Scoton, D.H. Oliveira, I. Mendes Gaya Lopes Dos Santos, F. Stallivieri, L. Cunha De Sousa

160. Enhanced Deep Learning Framework Driven Grape Berry Temperature Estimation and 72-h Forecasting for Precision Heat Stress Management

The increasing frequency of extreme summer heat events poses a significant threat to grape production in the Pacific Northwest (PNW), U.S., and globally. Elevated temperatures can induce sunburn, accelerate organic acid degradation, and cause anthocyanin loss, ultimately reducing berry quality. Berry surface temperature (BST), which can exceed ambient air temperature by up to 15 °C, is a primary indicator of heat stress severity. However, BST dynamics are governed by complex, nonlinear th...

161. Enhancing Corn Management with Variable Rate Fertilizer Maps: A Spatial Evaluation of Cover Crops

Farmers and agronomists need accurate, efficient methods for evaluating cover crop (CC) biomass and nutrient content to optimize fertilization strategies and improve soil health. Traditional biomass assessments are labor-intensive and time-consuming, often delaying timely data-driven management decisions. While multispectral cameras, including near-infrared (NIR) and RedEdge sensors, provide high-accuracy data for this purpose, their high cost limits accessibility for many farmers. This study...

162. Enhancing Pest Detection Through Spectral Signature Extraction in Hyperspectral Data

Detecting insect infestation is essential for effective crop protection, particularly in large-scale systems. Caterpillars and stink bugs induce physiological and structural alterations in plant tissues that can be captured through hyperspectral reflectance sensing, which is a non-destructive technique that measures plant responses across hundreds of wavelengths. However, raw spectral signatures are characterized by high dimensionality, strong inter-band correlation, and they often exhibit ba... A.O. Françani, J. Ferreira , L. Zhao, L.A. Jorge, K.M. De Oliveira, J.C. Felipe

163. Enhancing the Reliability of Portable Soil Probes Through Machine Learning Optimization

Precision agriculture requires high resolution soil data, yet traditional laboratory analyses limit sampling because of high human labour and analysis costs. Portable spectrographic tools allow rapid infield soil characterization with multiple measurements, although their accuracy often falls short of standard laboratory protocols. We hypothesized that machine learning (ML) models could improve the reliability of these tools by adjusting their outputs against laboratory reference values. ... A. Cambouris, E. Lord, M. Duchemin

164. Enhancing Weed Detection in Corn Crops Through Attention-based Models and Curated Datasets

Weed infestation is one of the leading causes of global agricultural productivity losses, directly impacting production costs, environmental sustainability, and food security. In precision agriculture, automated weed detection from aerial imagery enables site-specific herbicide application, reducing chemical overuse and environmental impact. Deep learning-based computer vision techniques have been widely adopted for this purpose, with Convolutional Neural Networks (CNNs) historically dominati... T.M. Martins, E.C. Tetila, J.G. Barbedo, J.C. Felipe, L. Zhao

165. Estimating Grape Bunch Yield Using Convolutional Neural Networks and Proximal RGB Imaging in the Brazilian Pampa

Viticulture of fine wines has become an increasingly important economic activity in the Pampa biome of southern Brazil, a relatively recent production frontier with approximately two decades of commercial development. In this emerging region, accurate prediction of grapewine productivity represents one of the most relevant challenges for growers, as reliable early estimates directly support decision-making related to harvest planning, logistics, labor allocation, and marke... S. Camargo, E.M. Da Silveira, F.I. Nogueira, A.F. Campos, V.Z. Mércio

166. Estimating Peanut Losses Using Machine Learning with Soil and Weather Data

Mechanized peanut harvesting is an important phase of the production system, directly affecting both production costs and crop yield. However, due to interactions among soil conditions, plant characteristics, and machine performance, this operation is carried out under challenging conditions that may result in high levels of loss. These losses are classified as visible when pods remain on the soil surface after digging and as invisible when they are incorporated into the soil profile, making ... A. Lopes De Brito Filho, F. Morlin Carneiro, G. Pereira Costa, B. Dos Santos Silva, P.H. Nogueira Gusmão, R.P. Pereira Da Silva

167. Estimation of Agronomic Parameters in Maize Using UAV-based Vegetation Indices Obtained by a Multispectral Sensor

Precision agriculture emerges as a response to optimize input use and to monitor crop spatial variability over time. In this context, the use of vegetation indices obtained by optical sensors embedded in drones or satellites has become a useful tool for predicting agronomic parameters in maize fields, such as aboveground dry biomass, leaf chlorophyll content, and grain yield. Thus, the objective of this study was to correlate field agronomic parameters with vegetation indexes obtained by a mu... B. Nogueira, A.C. Figueiró, A. , E. Bender, C.D. Lima, A.L. Vian, C. Bredemeier

168. Estimation of Broiler Chicken Mass using Computer Vision with Convolutional Neural Network

In poultry farming, monitoring bird mass during rearing is crucial, as it enables farmers to adjust parameters such as feed supply and lighting to better control weight gain. However, the methods currently used in poultry houses, i.e. manual weighing or poultry scales, present drawbacks, including the inability to weigh a representative number of birds or the frequent maintenance required to keep the equipment clean. The present work aims to validate an alternative method for estimating the m... I.D. Azevedo, A.T. Salton, R.D. Castro, L.V. Erthal

169. Estimation of carbon sequestration in agricultural crops using the C-Questro software

Contemporary agriculture faces the challenge of reconciling productivity with climate impact mitigation, positioning soil and plant biomass carbon sequestration as a strategic pillar for global sustainability. However, carbon quantification at the field scale still encounters hurdles due to high-cost methodologies or operational complexity. The objective of this work was to develop and validate a Python-based software, named "C-Questro," designed to automate the estimation of carbon...

170. Estimation of Leaf Area in Hydroponic Microgreenhouses by Digital Image Processing and Color-Based Segmentation

Monitoring plant development is a fundamental pillar of precision agriculture, as leaf area is a primary indicator of photosynthetic capacity, transpiration rates, and overall biomass accumulation. In controlled environments such as hydroponic micro-greenhouses, real-time growth monitoring enables timely adjustments to nutrient film technique (NFT) parameters. However, conventional leaf area measurement methods often rely on invasive sampling or expensive, high-maintenance optical equipment, ... M. Andrade, J. Da Silva Fonseca, R. Toledo, F. Martin Carbajal Gamarra

171. Estimation of Leaf Chlorophyll Index in Corn Using Smartphone Images and Machine Learning

Accurate estimation of the Leaf Chlorophyll Index (LCI) in corn is fundamental for nitrogen management in precision agriculture, as nitrogen availability directly affects chlorophyll production and photosynthetic capacity. Conventional field assessment techniques are time-consuming and labor-intensive, while smartphone use provides a practical and low-cost alternative for obtaining high-resolution data in near-real-time. The objective of this study was to develop and validate a non-destructiv... C.C. Santana, S.M. Mintesinot, D. Queiroz, F.S. Santos, A.L. Coelho

172. Estimation of Soybean Yield Using Remote Sensing and Soil Physical Attributes in Subsoiled Areas

Precision agriculture has incorporated these sensing and computational modeling technologies as strategic tools for monitoring crop development and estimating yield. In this context, the present study aimed to estimate soybean yield through vegetation indices obtained from satellite images, integrated with soil and plant variables, using artificial intelligence techniques. The experiment was conducted in a commercial field in the municipality of Brejo, Maranhão, in a region with a subh... W. Garreto, S. De Almeida, W. Da Silva Sousa, J. Costa Souza

173. Estimation of Sugarcane Yield Based on Phenological Feature Extraction from Time-Series Sentinel-1 Images and Machine Learning

Due to frequently rainy and cloudy weather in the main sugarcane production areas, optical remote sensing data are often missing, and the conventional yield estimation models based on radar remote sensing data lack the support of crop growth mechanisms. This study aims to explore a new yield estimation method for capturing the key dynamic growth features of sugarcane under all-weather conditions. This study takes the sugarcane yield in the dominant area of sugarcane production, Guangxi Zhuang... H. Xue, X. Xu, G. Yang, Z. Xu, S. Xiaoyu, L. Chen

174. Estimation of Weed Germination (Eleusine Indica and Amaranthus Spp.) Using Proximal Remote Sensing

Weeds are significant obstacles to the yield of commercial crops, leading to considerable economic losses worldwide. These plants compete directly with desired crops for essential resources such as water, light, and nutrients, and they can complicate agricultural management practices. In this context, early identification and understanding of the initial stages of weed development, such as germination, are crucial for enhancing management strategies and reducing both economic and environmenta... T. Prestes Pedroso , F. Morlin Carneiro, A. Lopes De Brito Filho, C.M. Teixeira Fialho, M.G. Da Silva Brochado, F. Morlin Carneiro

175. Europe Regional Meeting

... S. Pedersen

176. Evaluating APSIM for Precision Optimization of Planting Windows and Nitrogen Management in Maize-Soybean Intercropping Systems in Malawi

Maize-soybean intercropping is a key strategy for improving food security and resource-use efficiency in smallholder systems in sub-Saharan Africa. In Malawi, soybean promotion supports sustainable intensification, yet optimizing planting windows, spatial arrangements, and nitrogen (N) management under variable rainfall remains challenging. This study assesed the capability of  the Agricultural Production Systems Simulator (APSIM) to simulate maize-soybean performance and identify precis...

177. Evaluating Deep Learning Models for Image-Based Corn Kernel Detection, Counting and Yield Prediction

Accurate estimation of kernel number in corn is essential for evaluating yield potential in breeding and agronomic research. However, manual kernel counting is labor-intensive, prone to human error, and impractical for large-scale datasets, while most existing automated devices are limited to simple counting tasks. This study evaluates deep learning-based approaches for automated kernel detection and counting using You Only Look Once models and Faster R-CNN. Specifically, YOLOv8x, YOLOv10x, a... B. Ghimire, L. Lacerda, T. Bourlai, G. Lu

178. Evaluating On-Farm Variable Rate Seeding Trials with Causal Inference and Machine Learning.

Identifying field-specific economically optimal seeding rates (EOSR) is central to profitable crop production, yet conventional analytical approaches applied to on-farm trial data frequently conflate association with causation, limiting their utility for generating actionable site-specific management recommendations and constraining broader adoption of variable rate seeding (VRS) technology. Mixed-model ANOVA and quadratic response surface methods calculate a single average EOSR, masking spat... B. Adeyemi, Y. Miao, A. Kechchour

179. Evaluating Response-Based Management Units for Variable-Rate Nitrogen Application Using On-Farm Experiments

Site-specific nitrogen (N) management in precision agriculture is commonly based on management zones derived from soil properties and vegetation indices, implicitly assuming that spatial patterns in yield potential correspond to spatial patterns in crop response to N. However, yield level and marginal yield response to N represent distinct agronomic characteristics and may not coincide. This study evaluates whether conventional potential-based zones adequately capture spatial variability in N... C. Matavel, A. Meyer-aurich

180. Evaluating the Potential Benefits of Variable-rate Sulfur Management in Minnesota Corn Using Machine-learning Analysis

Sulfur (S) management in corn is complicated by strong within-field variability in crop response, driven by interactions among soil properties, landscape position, and prior management. As a result, uniform S applications can create unnecessary input costs in nonresponsive areas while undersupplying responsive zones. We developed and demonstrated a practical machine-learning (ML) workflow to estimate within-field agronomic optimum sulfur rate (AOSR) and economic optimum sulfur rate (EOSR) on ... R.P. Negrini, Y. Miao

181. Evaluation of an Ai-Driven High-Precision Spraying System for Targeted Weed Control in Onions

Weeds are a persistent challenge in the Vidalia onion production region, where limited herbicide options make placement and selectivity critical. AI-based high-precision spraying systems may reduce herbicide use and crop injury by targeting only weeds and non-crop areas. This study compared the Ecorobotix ARA high-precision sprayer operating in an all-but-the-crop mode with a conventional broadcast application for weed control, crop phytotoxicity, and onion performance. A field trial was cond... R. Dos Santos, L. Oliveira, L.D. Sales, M. Barbosa, C.T. Tyson

182. Evaluation of Convolutional Neural Network Architectures for Stress Detection in Eucalyptus saligna Using Multispectral UAV Imagery

Root malformation disorder (RMD) is a significant abiotic condition that impairs water and nutrient uptake in Eucalyptus saligna, leading to physiological stress and reduced stand uniformity. Early detection in commercial plantations is hampered by the extensive spatial scale and logistical limitations of manual field inspections. In this context, the present study evaluated the performance of three convolutional neural network (CNN) architectures, U-Net, U-Net++, and Attention U-Net, in dete... L.D. Amaral, S.D. Pereira Da Silva, R.A. Fantinel, V. Richter, N.

183. Evaluation of DC-based Equipment for Apparent Soil Electrical Conductivity Measurement

Apparent soil electrical conductivity (ECa) is a soil parameter   used for precision agriculture applications where   the knowledge of spatial variability of soil attributes is one important aspect to be known.  Researchers have concluded that ECa is a powerful for spatial heterogeneity characterization of several physico-chemical properties, identify edaphic and anthropogenic factors that may influence crop yield, and provides a viable approach for delineating areas ... R. Zandonadi, S.C. Rosa, D.S. Valente

184. Evaluation of Diffuse Reflectance Spectroscopy and Machine Learning Methods for Soil Available Phosphorus and Potassium Prediction

Phosphorus (P) and potassium (K) are essential elements for plants. Accurate evaluation of soil available P and K contents is fundamental for precision agriculture and site-specific nutrient management. However, traditional chemical analyses are time-consuming, labor-intensive, and costly. In this context, diffuse reflectance spectroscopy (DRS) has been introduced as a cheaper and rapid alternative; however, its accuracy in estimating soil P and K contents has not been fully proven, particula... C. Guerra Martins, D.L. Grando, J. Moura Bueno, G. Brunetto, A.A. Kokkonen, L. Peranzoni Deponti, L. Bastos

185. Evaluation of Filtering Approaches and Spatial Relationships with Gaps in Sugarcane Yield Maps

Yield maps represent indispensable tools in Precision Agriculture for the quantitative and qualitative characterization of crops. However, data derived from yield monitors do not always reflect actual field yield, as they are subject to measurement errors, operational errors, and/or equipment malfunctions. The presence of such distorted information compromises analytical accuracy and, consequently, decision-making. Given this challenge, the present study aimed to evaluate the performance of f... Y. De Lacerda Barbosa, V. Ferraz, E. Rafael Otavio Da Silva, J.P. Molin

186. Evaluation of Horizontal Distribution in Spraying with RPA in Static and Dynamic Modes

The application of plant protection products (PPPs) using remotely piloted aircraft (RPA) represents a significant innovation in the context of modern agriculture, especially regarding the pursuit of greater operational efficiency and the reduction of environmental impacts. The use of this technology has stood out for the possibility of carrying out more precise applications, with better control of droplet deposition and potential reduction in in input consumption. However, despite its promis... G. Gomes Mesquita , J.B. Costa Souza, I. De Oliveira Vieira, S.L. Hatum De Almeida, V.D. Carreira, R.P. Silva, A. Felipe Dos Santos

187. Evaluation of Irrigation Efficiency Using Precision Agriculture Tools on a Mandarin Orange Farm Afourer in Paysandú, Uruguay

Efficient water management in citrus farming requires monitoring systems that integrate real-time climate data and accurate soil moisture measurements to optimize irrigation decisions within a precision agriculture framework, accounting for spatial variability in soil water availability. The use of continuous monitoring technologies improves the efficiency of drip irrigation and reduces water losses associated with both water deficits and excesses in commercial production systems. Facing this... M.E. Cha Valdez, D. Boeno, J.I. Zapata , J. Duque

188. Evaluation of Kriging Models and Variogram Structures for Daily Weather Interpolation Across Georgia, United States

Spatial interpolation fills gaps between scattered weather stations to create continuous maps of variables like temperature. In Georgia, USA—a state with rolling hills in the north, coastal plains in the south, and the Appalachian foothills—this process is vital for accurate climate monitoring, irrigation scheduling, and crop-yield forecasting. Without reliable grids, downstream models suffer from bias or uncertainty. This study aimed to a...

189. Evaluation of Lettuce Image Classification with CNNs under Different NPK Nutritional Conditions

The growing global demand for food has driven the development of technologies aimed at increasing productive efficiency in sustainable agricultural systems, such as hydroponics. In this context, proper monitoring of nutrient solutions is essential, particularly for the early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, which directly affect lettuce growth, yield, and quality. Traditional nutritional diagnostic methods often rely on destructive laboratory analyses... E.L. Silva, E. Freitas, V.C. Secundino, D.G. Gomes

190. Evaluation of the DeepLab Family of Architectures in Segmentation Internal Brachiaria Seed Structures by X-ray Images

Seed vigor is an essential factor for the uniform emergence of seedlings and for the proper establishment of crops, being directly associated with the productive potential of agricultural crops. Accurate evaluation of this vigor is therefore fundamental for decision-making in seed management and production. Among the methods used for the analysis of physiological quality, the use of X-ray images stands out for allowing the non-destructive visualization of internal morphological structures of ... L.K. Gomes Maia, E. Freitas, W.V. Dias, J.F. Da Silva , B.D. Silva , H.F. Abud, D. G. Gomes, P. Dos Santos E Silva

191. Evaluation of the Performance of Computer Vision Models in the Detection and Counting of Tomato Plants Infected by Tomato Spotted Wilt Virus (TSWV)

Tomato is one of the most economically important vegetable crops worldwide. However, this crop is severely affected by Tomato Spotted Wilt Virus (TSWV), whose transmission occurs mainly through thrips. Thus, identifying infected plants is an important step to reduce the dissemination and infection of healthy plants, reducing economic losses. Computer vision-based models have been widely used in the automated detection of plant diseases. In this context, this work aimed to evaluate the perform... L.S. Souza Pinto, S. . Azevedo, M. . Medeiros, A. Felipe Dos Santos, T. Costa Barboza, M.C. Arnosti, G. Valdes Fernandez , G. Lacerda Da Silveira

192. Evaluation of the Similarity between Management Zones Based on Spectral Indices and Soil Electrical Conductivity

The delineation of management zones is one of the main strategies in precision agriculture, as it enables site-specific interventions and improves input-use efficiency. Soil electrical conductivity (EC) is widely used to represent the spatial variability of soil physical and chemical attributes and is often considered a reliable indicator. However, acquiring EC data requires specific equipment and field operations, which may increase operational costs. In contrast, spectral indices derived fr...

193. Evaluation of Transfer Learning in Semantic Segmentation Models for Soybean Seedlings

Seed vigor evaluation is fundamental in the quality control of commercial lots, as it is directly associated with the rapid and uniform emergence of seedlings and the initial performance of crops in the field. Traditional methods, although widely used, present limitations such as long execution time, dependence on the evaluator’s experience, and subjectivity. In this context, systems based on Computer Vision emerge as promising alternatives for automating vigor assessment, as they enabl... E. Freitas, J. Martins Neto, P. Dos Santos E Silva, H.F. Abud, D.G. Gomes, V.C. Secundino

194. Evaluation of Variable and Uniform Rate Prescriptions of Potassium Fertilizing for Small Plots in Family-run Coffee Farms

Brazil plays a central role in the global coffee supply as one of the primary providers for strategic international markets. As extreme weather threats, prolonged droughts, and international agricultural commodity price volatility increase, enhancing economic efficiency in input use has become fundamental for the sustainability and resilience of coffee production systems. Thus, efficiency in the use of agricultural inputs transcends economic concerns, becoming part of the broader discussion o... N. De S. Ludovico Almeida, B.S. Costa, J.L. Favarin, J.P. Molin

195. Evaluation of Variable-Rate Application of Growth Regulator in Cotton

The application of plant growth regulators in cotton is widely used to control excessive vegetative growth and improve plant architecture. However, the spatial variability present in agricultural fields may reduce the efficiency of conventional fixed-rate application, making variable-rate application (VRA) a promising alternative. The objective of this study was to evaluate the effect of fixed-rate and variable-rate application of the growth regulator mepiquat chloride on plant height, leaf a... B. Costalonga Vargas, M.R. Furtado Junior, G.O. Paula, A.L. Coelho, M.C. Moreira, F.R. Carvalho

196. Evaluation of Vertical C/N Distribution in Maize Canopy Using Ensemble Learning with Hyperspectral Data

Metabolic status of carbon (C) and nitrogen (N) as two essential elements in crop plants has essential influence on the ultimate formation of yield and quality in crop production. Ratio of carbon to nitrogen (C/N), defined as the ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is useful for understanding and quantifying carbon and nitrogen metabolism in crops, and is one metrics for effectively evaluating the balance of carbon and nitrogen, nutrient status and g... X. Xu, X. Xu, Y. Meng, G. Yang, Y. Song, H. Xue

197. Evolution and Potential of Digital Agriculture: The Experience of SLC AgrÍcola

... A. Pavinato

198. Expanding Access to High-Resolution Soil pH Measurement: From Smallholder Farms to 200-Hectare Fields

Soil pH is one of the most influential and correctable soil properties affecting crop productivity. It governs nutrient availability, toxicity risk, microbial activity, and overall soil function. Yet timely and spatially representative pH measurement remains inconsistent across agricultural systems due to laboratory turnaround times, sampling costs, limited field access, and sub-field variability not captured by conventional sampling density. This study evaluated multiple strategies... T. Lund, C. Maxton, E. Lund

199. Exploring the XAG R150 UGV Capabilities: Air-Assisted vs Vertical Boom Attachment Assessments

Robotic spraying platforms are rapidly advancing in specialty crop production; however, limited information exists regarding optimal spray parameters under different crop spacings and spray configurations. The XAG R150 robotic sprayer is a ground-based autonomous system capable of operating in both air-assisted and vertical/horizontal boom spraying modes, offering flexibility for diverse row-crop environments. This study evaluated spray performance parameters of the XAG R150 under varying cro...

200. Farm-level Economic Viability Site-specific Weed Management (SSWM)

Background: Site-specific weed management (SSWM) is promoted as a strategy to reduce herbicide inputs without compromising crop yield. Despite rapid advances in offline and online AI-based weed mapping, the farm-level economic viability of SSWM under real-world weed patchiness and imperfect detection remains uncertain. Methods: A grid-based (1x1m, 50x50cm and 25x25cm) economic decision model for SSWM was developed and evaluated using 31 empirical weed... M. Gandorfer, J. Pfrombeck, V. Pitsyk, D. Pannell

201. Farmer-centric OFE: What’s in it for Scientists?

Farmers experiment on their farm every year to learn how to use new inputs, techniques and technologies, but also to invent new ways to farm. This has been the process through which agriculture has evolved over the last millennia; they do it whether or not scientists are involved.  Traditionally, scientists have not been involved because of the lack of scientific rigor in farmer-led OFE. However, digital agriculture opens new possibilities for learning from farmer-centric OFE. But why wo... L. Longchamps, M. Lacoste, K. Rohrbaugh

202. Field-Based Evaluation of Targeted Herbicide Spraying Efficacy: A Comparison of Qualitative and Quantitative Approaches

Weed control remains one of the main challenges for maintaining agricultural productivity. In this context, selective spraying based on optical sensors and embedded vision systems emerges as a promising alternative for localized weed management, aligned with the principles of precision agriculture and sustainability. However, the adoption of these technologies on a commercial scale demands robust methods to evaluate agronomic efficacy and operational performance under real field conditions. T... R. Luiz Panini, A.R. Tamara, G.M. Franco, V.C. De Oliveira, H.R. Lemos, M. Nishikawa, P.R. Forti, M. Biagi, L.F. Dudek, M.C. Hauschild, G.N. Ruscito, M.P. Da Silva, E. Claro, Z.M. De Souza

203. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates

The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron

204. Flow Rate and Discharge Coefficient Behavior of Spray Nozzles in a PWM System

Pulse width modulation (PWM) technology has been widely used in variable-rate pesticide applications, allowing flow control by varying the duty cycle while maintaining constant pressure. However, the intermittent operation of the flow may alter the hydraulic behavior of spray nozzles, influencing both the effective flow rate and the discharge coefficient (Cd). In this context, the objective of this study was to evaluate the behavior of flow rate and discharge coefficient of different spray no... B. Costalonga Vargas, M.R. Furtado Júnior, A.L. Coelho, S. Basilio

205. From Area-wide Management to Site-specific and Individual Plant Management: the Evolution of Weed Control in Argentina

Selective herbicide spraying represents one of the most significant innovations in the evolution of Precision Agriculture, enabling a shift from uniform broadcast applications toward spatially explicit and plant-by-plant management models. In a context characterized by the increasing prevalence of herbicide-resistant weed biotypes, rising input costs, and growing environmental and regulatory pressure, these technologies are becoming strategic tools to enhance input-use efficiency and improve ... I. Lo Celso, N. Ciancio, F.M. Scaramuzza

206. From Pocket to Pixels: Smartphone-Based Proximal Sensing for Quantitative Monitoring of Peach Canopy Dynamics

Monitoring peach canopy dynamics is essential for optimizing irrigation scheduling and promoting sustainable water management in intensively managed orchards. In well-watered systems, conventional irrigation practices often overlook spatial and temporal variability in canopy size and leaf area index (LAI), potentially leading to inefficient water use. Canopy size, as reflected by LAI—the ratio of total leaf area to ground area—serves as a key biophysical indicator linked to crop t... L. Katz, K. Genkin, A. Landau, Y. Ness, O. Crane, R. Golan, N. Rotbart, E. Nevo, O. Shapira, E. Fereres, O. Reichmann, A. Brook

207. From Raw Yield Data to Cell-based Risk Management: a Next-generation Framework for Ultra-high Resolution Yield Stability and Yield Gap Analysis in Hungarian Arable Environment

A fundamental challenge in modern precision agriculture is ensuring access to raw yield data, its systematic processing, and the subsequent planning of foundational and Variable-Rate Application (VRA) maps required for decision support. This research aimed to develop a methodology suitable for the cell-level multitemporal processing of yield datasets to establish production-risk classes and quantify unrealized potential via yield gap analysis. This framework exceeds conventional precision pla... D. Szám

208. From Render to Field: Detecting Asian Soybean Rust Using Models Trained Exclusively on Synthetic Imagery

Training machine learning models for crop disease detection requires large, annotated datasets that are costly and difficult to obtain under variable field conditions. Asian Soybean Rust (ASR) can reduce soybean yield by up to 90% and costs Brazilian producers over US$2 billion per season in fungicide applications and yield losses. Despite this impact, existing machine learning studies on ASR remain scarce with no publicly ... L.B. Fontoura, A. De Freitas, E. Farinati Leite, J. Valiati

209. GAIG: High‑Resolution Wall‑to‑Wall Modelling of Within‑Field Spatial Variability in Crop Yield

Quantifying the temporal stability and causes of within‑field variation in crop yield is fundamental to precision‑agriculture research, particularly as agricultural areas seek to identify lands with persistently low productivity that may constitute marginal cropland. What is needed is a modelling framework capable of using spatial patterns in yield to reveal stability zones, diagnose sources of variability, and enable consistent comparison across farms and years. Accordingly, the objectiv...

210. Generating Data Via Operational Monitoring of Backpack Equipment on Small Farms

The Brazilian coffee industry, a global leader in production and exports, faces the challenge of increasing production efficiency to meet growing worldwide demand while preserving natural resources. Precision Agriculture (PA) offers essential tools for this data-driven sustainable intensification; however, its adoption in regions with rugged topography and by family-based growers is severely limited by the scarcity of accessible technologies, particularly for spatial yield measurement. The se... V. Ferraz, L. Mariotto Nabarro, R. Castanho Fernandes, B.B. Barreto, B. Ricardo Silva Costa, J.P. Molin

211. Generation of a Digital Terrain Model in Areas with Dense Vegetation Cover for the Identification of Erosive Processes from LiDAR Data Obtained with a Matrice 300 Drone

The analysis of relief, as a conditioning element of surface processes, depends on its adequate representation through Digital Terrain Models (DTM), especially in studies aimed at identifying erosive processes. In this context, the use of LiDAR sensors makes it possible to detect terrain features even under dense vegetation cover, overcoming the limitations of optical sensors. Thus, this research aimed to demonstrate the applicability of the Matrice 300 RTK drone, equipped with the LiDAR L1 (... P. Rezende, R. Fernandes De Queiroz, H.A. Machado, D.S. Freitas

212. Generation of Ultra-High-Resolution Synthetic Data via Generative Super-Resolution to Support UAV Image Annotation and Model Training

Manual annotation of imagery acquired by unmanned aerial vehicles (UAVs) for detection/segmentation tasks is one of the main bottlenecks for deep learning applications in precision agriculture, due to the high cost and the time required to produce consistent labels. In addition, low-altitude flights to obtain ultra–high spatial resolution increase operational complexity and data volume, limiting the scalability of acquisition campaigns. Although neural network–based super-resoluti... M.A. Karasinski, R. Costa, C. Melville, E. Macedo, I.L. Gabriel Da Silva Carmo , S.V. Dantas Oliveira, M.P. Galvão, A.B. Bendahan, C.R. Bezerra

213. Generative AI Applied to Precision Agriculture

A practical and straight-to-the-point workshop aimed at agronomists, consultants, researchers, and managers who want to use Generative Artificial Intelligence to transform agricultural data into faster, more technical, and more accurate decisions. During 3 hours of immersion, participants will learn how to apply generative AI tools in the routine of Precision Agriculture (PA) — from analyzing maps, sampling plans, and productivity spreadsheets to creating agronomic recommendat... G.M. Sanches

214. GEODATA - Hidden Flaws in Precision Agriculture Practices for Soil Fertility Management: How Small Decisions Can Jeopardize the Entire Effort

... R. Almeida

215. GEODATA - Precision Agriculture Beyond Maps: How Precision Ag Companies Are Generating More Value for Producers

... V. Saque Ribeiro

216. Geometric Assessment of Software-Based Path Planning for Mechanized Seeding Operations

This study evaluated the impact of different software-based planning routines on the geometry of guidance lines for mechanized seeding operations. A controlled comparative case-study approach was implemented using two agricultural fields in Minas Gerais, Brazil. One irregular field of 33.6 ha and one predominantly rectilinear field of 107.6 ha. Two anonymized tools, Software A and Software B, were applied to identical boundary polygons, with one headland pass and 9 m line spacing. The exporte... A. Felipe Dos Santos, R.D. Borges, M.C. Arnosti, C. Marcassa Lonzi De Oliveira

217. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions

Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fe... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

218. Growth-Stage and Hourly Modeling of Non-Stressed Soybean Canopy Temperature Using High-Frequency Proximal Thermal Sensing

Canopy temperature (Tc) sensing provides a proximal, non-destructive approach for monitoring crop water status. It supports irrigation scheduling through thermal indices such as the Crop Water Stress Index (CWSI) and Degrees Above Non-Stressed (DANS), both of which require accurate estimation of non-stressed canopy temperature (Tcns) (Nakabuye et al., 2022). Maintaining a continuously non-stressed reference treatment to determine Tcns is operationally difficult, motivating development of weat...

219. Guiding Spot Sprayer Decisions: Toward Species-Selective Weed Control

Site-specific weed management is a key approach in precision crop protection, enabling spatially targeted herbicide application based on within-field variability in weed distribution. However, most operational spot-spraying systems rely on uniform nozzle activation rules, implicitly treating all detected weeds equally despite differences in competitive ability and ecological function. This limits the potential of precision systems to exploit species-level differentiation in practice. ... M. Gentili, M.S. Madsen, V.A. Nichols, R.N. Jørgensen, J.R. Jørgensen,

220. Hardware–Software Co-Design of Quantized CNN Inference for Edge AI in Precision Agriculture

Precision agriculture increasingly relies on real-time automated inspection systems to ensure crop quality and reduce manual labor in grain handling processes. Manual visual inspection, traditionally used for grain quality assessment, is inherently limited by low throughput, subjectivity, and high labor costs. To address these issues, automated vision-based inspection systems have been widely adopted in industrial environments, enabling high-throughput and consistent grain classification. Rec... E. Marañon Aguilar, F. Kastensmidt, F. Benevenuti, C. Gonzalez Aguilera

221. Harmonic Modeling of Coffee Biennial Bearing to Quantify Between-plot Variability: a Precision Agriculture Approach for Small-scale Agriculture

Precision Agriculture (PA) practices rely on detecting spatiotemporal variability within a plot to delineate subplots by pixels or by management zones (MZs). This paradigm has been assumed for large-scale plots, yet their adoption in smallholder systems, such as coffee crops under family-based agriculture, remains limited. In this context, within-plot variability is often less operationally relevant than between-plots divergency. Therefore, we assume each plot as a MZ and focus on manage them... J.P. Molin, B. Costa, B. Barreto

222. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in Soybean

The conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed c... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian

223. High Resolution 3D Crop Analysis and Decision Support using UAV LiDAR Technology

Agriculture is one of the most significant economic activities in Brazil, with coffee cultivation playing a particularly prominent role in the state of Minas Gerais, which leads national production. During the early stages of the coffee growth cycle, systematic monitoring practices are required to assess the spatial uniformity of plant development. These observations support management decisions, including the identification of areas requiring specific interventions and the targeted applicati... A. Pavanelli, L. Carneiro De Souza, C.E. Inácio, M. De Oliveira, V. Rennó, F. Portelinha, L. Mendes

224. High-resolution Orbital Imagery and Neural Networks to Predict Brix and Purity in Sugarcane

Integrating artificial neural networks with high-resolution satellite remote sensing data can provide non-destructive indicators for assessing sugarcane quality at field scale. Conventional laboratory methods for sucrose-related quality assessment are costly, labor-intensive, and operationally demanding, particularly when applied continuously over large commercial areas. This study evaluated the potential of multispectral imagery from the PlanetScope CubeSat platform, vegetation indices, and ... P. Cardoso, R.P. Silva, T.R. Da Silva, M.F. De Oliveira, J.B. Souza, S.L. De Almeida

225. High-Sensitivity Flexible LIG/GO Humidity Sensors for Continuous Environmental Monitoring in Agricultural Applications

The efficiency of agricultural production depends substantially on the continuous monitoring of environmental variables, particularly humidity, which directly influences plant physiological processes, the physicochemical properties of soil, and the preservation of plant materials in the post-harvest stage. This study presents the development of a capacitive humidity sensor based on laser-induced graphene (LIG) and graphene oxide (GO), characterized by its flexibility and adaptability to diffe... A. La Rosa, P. Silveira, B.B. Gallo, B.V. Lopes, L.M. Goncalves, N. Carreño

226. How Does Yield Data Filtering in Grain Harvesters Influence the Quality of Interpolated Maps?

Yield maps generated from grain harvester data are effective tools for characterizing the spatial variability of crop yields. However, several embedded errors are inherent in these datasets, requiring removal methods to ensure the fidelity of actual field yield values and the reliability of the resulting maps. Therefore, this study aimed to determine the optimal combination of parameters for the global and local filtering of grain harvester yield maps to improve the quality of interpolated ma... A. Andrade Da Silva, E. Sales, T. Moura Oliveira, R. De Souza Silva, S. Luns, R.P. Silva, E. Perussi

227. Hybrid Fuzzy–pid Control for Variable-rate Center Pivot Irrigation: an Automation-driven Approach to Precision Water Management

Precision agriculture increasingly relies on advanced automation and intelligent control strategies to address the spatial and temporal variability of crop water requirements while minimizing resource consumption. Center pivot irrigation systems are widely deployed in large-scale farming operations; however, their conventional control architectures are typically based on fixed schedules or linear feedback laws, which are insufficient to handle the nonlinear dynamics, uncertainties, and distur... F.R. Jimenez Lopez, A. Jimenez, I.A. Ruge Ruge, D.Y. Garcia Ramirez

228. Hyperspectral Imagery for Prediction of Leaf Chlorophyll Content in Maize Under the Application of Different Urease Inhibitors Using Machine Learning

Urea is the most common and widely used nitrogen (N) source. However, it is highly susceptible to ammonia volatilization losses, especially under favorable climatic conditions. The use of urease inhibitors becomes an important strategy because these compounds slow down the hydrolysis of urea, increasing efficiency in terms of N assimilation, enhancing leaf chlorophyll content, promoting plant growth, and maximizing maize grain yield. In parallel, hyperspectral sensors have emerged as a non-de... S.R. Gonçalves Junior, M. Da Costa Salem, G. Eissmann Souza, D. Carvalho De Arruda, E. Bender, B. Nogueira, L. Espindola Muller, B.B. Gallo, C. Bredemeier

229. IBRA MEGALAB - From Soil Maps to Precision Irrigation: Transforming Data into Decisions

... R. Battist

230. IBRA MEGALAB - From the Laboratory to the Field: The New Era of Digital Soil Maps

... P. Garcia

231. IBRA MEGALAB - Soil Health Plan: Integrating Diagnosis and Monitoring

... T. Parducci Camargo

232. ICICLE: A Generic Cyberinfrastructure Pipeline for AI-Driven Digital Agriculture Processing

Digital agriculture suffers from fragmented data and processing tools, restricting our ability to derive consistent, scalable insights that support precision management.  The NSF ICICLE (Intelligent Cyberinfrastructure with Computational Learning in the Environment) project addresses this challenge by developing a generalized cyberinfrastructure framework for AI‑enabled data processing across diverse production systems. Deployed at The Ohio State University, ICICLE provides a unif... H. Subramoni, S.A. Shearer, J.P. Fulton

233. Impact Assessment Tool (IAT) for Robotic and XR Applications in Agriculture: Cases from AgRibot Project

This paper presents an Impact Assessment Tool (IAT) developed within AgRibot project which is meant to evaluate the socio-economic and environmental impact of AR/XR-integrated robotic applications in crop farming based on six use cases situated in selected European countries. While each case focuses on a target crop and operation, the impact assessment follows a modular approach with some modifications according to case specificities. The tool aims to address the challenges of quant... T.W. Tamirat, S.M. Pedersen

234. Impact of Mechanical Decompaction on Soybean Yield through Remote Sensing and Agronomic Variables

Soil compaction is one of the main limiting factors for agricultural productivity, requiring efficient diagnostic and management methods. Increased machinery traffic in agricultural areas alters soil physical properties, reducing macroporosity and increasing soil penetration resistance, which restricts root growth and limits water and nutrient uptake. Traditional field methods for diagnosing soil compaction are often punctual and labor-intensive, and they may not adequately represent the spat...

235. Impact of Sampling Density on the Spatial Prediction of Soil Chemical Attributes Using Geostatistics and Machine Learning

Soil sampling at high grid densities represents a significant economic barrier to the adoption of Precision Agriculture (PA) in Brazil. This study evaluates the trade-off between sampling density and interpolation quality by comparing geostatistical methods and machine learning algorithms. Three distinct approaches were statistically assessed: Ordinary Kriging (OK), Random Forest (RF), and the hybrid Random Forest Regression Kriging (RFRK). The analysis was conducted across six fields totalin... S. Ribeiro, H. Fantin Gebler, J.P. Molin, R.F. Da Silva

236. Impact of Soil Heterogeneity and Precision Air Seed Drill Settings on Maize Emergence Uniformity: Toward Sensor-based Predictive Models

Soil spatial heterogeneity at the intra-field scale strongly constrains crop establishment. This heterogeneity in soil texture, strength, and moisture often leads to uneven seed-soil contact and inconsistent emergence, reducing yield potential. Extreme climatic conditions, such as soil saturation or drought, amplify these challenges and further compromise uniform crop emergence. Although this issue is widely acknowledged, accounting for soil h... A. Veiga, H. De Araujo, L. Tetard, M. Faucon, C. Ugarte

237. Impact of Telemetry Data Preprocessing on the Accuracy of Fuel Consumption Predictive Models in Heavy-Duty Truck Transport of Sugarcane Stalks

Fuel consumption efficiency in biomass transport is a determinant factor for the sustainability of agribusiness. However, agricultural machinery telemetry data present intrinsic challenges, such as onboard sensor noise and inconsistencies. This study aimed to demonstrate that meticulous data preprocessing is more relevant than algorithmic complexity in achieving high-performance predictive models. The raw dataset contained 43,112 trips by trucks responsible for transporting sugarcane stalks f... L.E. Zonfrilli, A.R. Camolesi, T. Canata

238. Importance of Irradiance Correction for UAV-based Vegetation Indices in the Prediction of Shoot Biomass in Wheat

The extraction of vegetation indices from multispectral images obtained with UAV-bsed sensors for biomass estimation has proven to be a useful tool for designing site-specific interventions on wheat. The reflectance values from monochromatic bands collected with optical sensors for calculating vegetation indices should have high reliability and repeatability in the case of successive assessments under different illumination conditions. In this context, the objective of the study was to charac... A.C. Figueiró, B. Nogueira, E. Bender, R. Silva, V.M. Cassol, S.J. Silveira , C. Bredemeier, A.L. Vian

239. Improving In-season Corn Nitrogen Status Prediction using Satellite Remote Sensing and Foundation Models with Agronomic Constraints

Precision nitrogen (N) management in maize requires in-season estimates of crop nitrogen status that are both accurate and physiologically credible, yet agronomic training data are often limited because destructive sampling is expensive and spatially sparse. Mechanistic crop models respect physiology but require extensive calibration and are computationally costly at field scale, whereas purely data-driven machine learning using remote sensing can achieve good accuracy while producing implaus... A. Kechchour, Y. Miao, V. Sharma, D. Mulla

240. INCERES - High-Risk Harvest: Where to Put Your Money When Wather, Interest Rates, and Margins Tighten

... L.

241. Individualization of Banana Canopies Using Multispectral Vegetation Index and the Watershed Algorithm

The individualization of canopies in perennial crops is an essential step in precision agriculture, enabling plant counting, vigor monitoring, yield prediction, pest management, and harvest planning. Banana (Musa spp.), characterized by large leaves, closed canopy, and high biomass, presents specific challenges for automated segmentation. This study evaluated the performance of the watershed algorithm for canopy individualization, using different multispectral vegetation index, betwe... B.R. Costa, J.P. Molin, B. Barreto

242. Influence of Application Rate and Flight Orientation on Droplet Deposition by Remotely Piloted Aircraft (RPA) in ‘Gala’ Apple Orchards

  Apple production is a cornerstone of the fruit industry, with the ‘Gala’ variety holding a prominent position due to its commercial value. However, this cultivar is highly susceptible to apple scab (Venturia inaequalis), requiring application technologies of fungicides that ensure canopy protection, especially during flowering. In this sense, precision horticulture has sought alternatives to optimize the application of crop protection products, aiming to ... L. Espindola Müller, T. Buchener, B. Nogueira, R. Silva, S.J. Silveira, C. Bredemeier

243. Influence of Meteorological Variables on Bean Yield in the Semi-Arid Region: A Data-Driven Approach for Agricultural Decision Support

Common bean is a strategic crop for the Brazilian semi-arid region, predominantly cultivated under rainfed systems that are highly dependent on climate variability. In regions characterized by irregular rainfall patterns, high temperatures, and extreme weather events, incorporating temporal analyses based on meteorological data becomes essential for evidence-based agricultural planning. Within the context of precision agriculture, the integration of historical climate series and productivity ... A. Fonseca, J.F. Dos Anjos, E.F. Da Silva, G.B. Moura, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, A.C. Bezerra

244. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models

Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral prep... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

245. Influence of Terrain Attributes on the Spatial Variability of Soil Macronutrients in Vineyards of Southern Brazil

Nutrient variability in vineyards directly affects grapevine development and grape yield, highlighting the importance of appropriate nutritional management, since optimizing soil nutrient levels contributes to improved grape, and, consequently, wine quality. The objective of this study was to evaluate the spatial variability of soil macronutrient distribution in a vineyard and to correlate it with terrain attributes. The study was conducted in a 10-ha commercial Pinot Noir vineyard located in... A. Costa Tolfo, B. Trevizan Paese, J.M. Moura Bueno, A.A. Kokkonen, G. Brunetto, S. Schemmer

246. Influence of the Type of ANN Algorithm on Prediction of Georeferenced Sugarcane Quality

Optimizing crop quality and yield is critical to the development of more sustainable agriculture on large scale. Predictive models can provide assessment of those attributes prior to harvesting using techniques of artificial intelligence to support site-specific management. The objective was to investigate the influence of ANN (Artificial Neural Network) algorithms on prediction of sugarcane quality. Brix content of sugarcane, variety CTC 2994 on second ratoon, was measured in laboratory usin... T. Canata, F.P. Monachesi, O.D. Salomão, L. Rodrigues , L.E. Zonfrilli, H.C. De La Cruz, P.F. De Mello

247. Innovations in Agricultural Robotics

Weeds have long constrained high-quality crop production, whilst prolonged reliance on chemical weed control has raised concerns over pesticide residues, environmental pollution, enhanced herbicide resistance in weeds, and declining agricultural sustainability. Laser weed control, characterised by non-contact operation, residue-free treatment, and strong targeting capability, is emerging as an important physical weed control approach in precision and green agriculture. This review emphasises ... W. Su

248. Innovations in Fertilizer Recommendations in the Context of Precision and Digital Agriculture: The Fertilizer Recommendation Support Tool

... L. Gatiboni

249. Innovations in Precision Irrigation

... G. Vellidis

250. Integrated Evaluation of Precision Spraying Systems in Pecan Orchards: Smart Apply System™, Conventional Airblast, and Drone Application

Pecan production in Georgia faces increasing challenges related to weather variability, rising labor costs, and higher prices of agricultural inputs, particularly fungicides required for scab (Venturia effusa) management. Conventional air-assisted sprayers operate at fixed application rates and do not account for tree size variability or canopy gaps, often resulting in overapplication and reduced efficiency. Emerging precision technologies, such as Smart Apply™ sprayers and spr... R.P. Oliveira, J.P. Silva, J.V. Martins, R. Dos Santos, M. Barbosa, L. Oliveira

251. Integrated Production Systems and Low-Carbon Agriculture in the Context of Agriculture 4.0

... A.C. Bernardi

252. Integrating Data Layers with Machine Learning to Predict Yield for Irrigated Grain Crops within Management Zones

Effective yield prediction is fundamental for precision agriculture, enabling data-driven management decisions. This study, conducted in a 52.3 ha center-pivot irrigated field in Itaí, São Paulo, Brazil, evaluated the hypothesis that delineating management zones (MZs) based on stable soil and terrain attributes, combined with machine learning (ML) algorithms, improves grain yield prediction accuracy compared to field-scale models. Apparent soil electrical conductivity (ECa) at t... L. De Goes Sterle, R. Canal Filho, V. Ferraz, M. Gelain, J.P. Molin

253. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting

The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a pea...

254. Integrating Proximal Hyperspectral and Machine Learning to Predict Nitrogen in Short- and Full-stature Corn Hybrids at Early Growth Stage in Indiana, USA

Nitrogen (N) fertilizer use is a complex challenge, as underapplication can harm yield and overapplication can harm profitability and the environment. N accounts for roughly 58% of total US corn fertilizer use (an annual expense of ~$8 billion), with overapplication estimated at 15% ($1.2 billion for possible savings). Within this setting, early-season yield prediction is a high-value capability for breeding and farmers. If plot-level plant N can be forecasted with high accuracy before the co... B. Paulus Scheffer, D. . Quinn, P.H. Magalhaes Cisdeli, J. Jin, Z. Qin, I. Ciampitti

255. Integrating Tractor-tire-tool Adjustable Parameters and UAV‑derived Soil Indices to Predict Fuel Consumption and Crop Emergence in Spring Barley Sowing

The optimization of energy use and agronomic performance in agricultural operations has become a central challenge in modern agriculture. To achieve this dual objective, farmers could adjust the machinery settings of a tractor-tire-tool system to ensure efficient resource utilization while maintaining optimal agronomic outcomes. This study was conducted as a part of the AgrEnOp project, which aims to predict fuel consumption (l/ha) and crop emergence (plant/m2) based on operator-ad... D. Urbina Salazar, A. Yatskul, F. Pinet, A. Dujany, C. Ugarte

256. Integrating Variable Rate Nitrogen Fertilization and Traffic Intensity Thresholds for Site-Specific Management in Mechanized Sugarcane Systems

Precision Agriculture (PA) has primarily focused on site-specific input management to improve resource-use efficiency; however, in highly mechanized cropping systems, soil physical degradation induced by machinery traffic remains a critical constraint to system performance. This study integrates two complementary approaches developed in commercial sugarcane production systems in Colombia: (i) variable rate nitrogen fertilization (VRT) as a strategy for rational fertilizer use, and (ii) a maxi... O. Chaparro Anaya, S. Saavedra Rincon

257. Integration of LiDAR-Derived TWI and UAV Multispectral Data for Waterlogging Susceptibility Mapping in Precision Agriculture

Topography directly controls water redistribution across the landscape, shaping the spatial variability of soil moisture in agricultural areas. The Topographic Wetness Index (TWI), derived from digital elevation models, is widely used to estimate the potential for water accumulation; however, its field-scale validation supported by high-resolution multispectral drone imagery remains limited. In agricultural systems, recurrent waterlogging can reduce productivity by impairing germination, prom...

258. Integration of Spectral Phenological Markers and Artificial Neural Networks for Modeling the Yield of Potato Cultivars

The growing demand for food underscores the importance of essential crops such as potato. In this context, understanding yield dynamics is critical, and digital agriculture emerges as a key tool, enabling more efficient estimation of this variable without the need for destructive sampling. Accordingly, this study aimed to use orbital remote sensing combined with artificial intelligence algorithms to develop more accurate and precise models for potato yield prediction. Field data collection wa... S. Luns, J.B. Souza, B. , L. Conceicao Da Silva, R.P. Silva, V. Carreira

259. Integration of Spectrotemporal Metrics and Machine Learning for Soybean Grain Yield Prediction

Estimating agricultural grain yield in heterogeneous production environments remains one of the main challenges of digital agriculture. Although vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge (NDRE), are widely used to describe canopy vigor, their predictive capacity strongly depends on how temporal information is represented and on the structure of the model employed to integrate this spectral variability. The present stud... L. Rossetto Gerlach, A.L. Vian, C. Bredemeier, T. Enderle, T. Santos Cocco, M. Wrubleski

260. Interaction Between Biological Nitrogen Fixation and Urea Rates on Nitrogen Accumulation and Yield in Maize

Nitrogen (N) is a fundamental nutrient for maize, with a direct impact on the crop’s yield potential, and it represents one of the highest costs in the production system. The possibility of biologically fixing N emerges as a potential strategy for managing crops in a more economical and efficient manner, reducing dependence on mineral nitrogen fertilizers and promoting greater physiological balance in the crop. A wide range of biological products has been introduced with the aim of comp... T. Enderle, T. Santos Cocco, M. Wrubleski, L. Rossetto Gerlach, A.L. Vian

261. Inversion of Potato Chlorophyll Content Based on Radiation Transfer Model and Machine Learning Algorithm

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. However, the performance of traditional LCC inversion models is limited by the quality and scale of training data. It is difficult to satisfy the needs of precision agriculture. 【Objective】Therefore, this study proposes a hybrid modeling framework base... Y. Ma, J. Zhang, D. Pan, Q. Wu, S. Xiaoyu, X. Xu

262. ISPA Economics Community Meeting

... M. Michels, M. Gandorfer

263. ISPA Nitrogen Management Community

... L.A. Puntel, L. Bastos

264. JACTO - Artificial Intelligence in Agriculture

... R.

265. JOHN DEERE - Spotlight

...

266. Large-Scale Sugarcane Yield Prediction Across Regions by Integrating Multi-Source Remote Sensing and Machine Learning

Sugarcane (Saccharum officinarum L.) is one of the most important agro-industrial crops worldwide, playing a key role in sugar, bioethanol, and renewable energy production. Early and accurate yield estimation during the growing season is essential to support agricultural planning, resource management, and decision-making in the sugar-energy industry under increasing climate variability. However, most yield models are calibrated to single locations and struggle to transfer across regions. The ... R. Fortes Gallego, F. Serra Burriel, M. Cabrera Dengra, C. Ferraz, A. Do Vale Dondo

267. Latin America and the Caribbean Regional Meeting

... R.P. Pereira Da Silva

268. Leaf Nutrient Estimation in Soybean from Multispectral and Multitemporal Information Using UAV and Machine Learning

Precision agriculture, through remote sensing with Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence, offers solutions for monitoring crop growth and development, supporting decision-making aimed at resource optimization and agricultural sustainability. This study evaluated the feasibility of using multispectral information captured by UAVs at different phenological stages (V6, V8, and R2) to estimate leaf nutrients (N, P, K, Ca, Mg, Cu, Zn, and Mn) in soybean crops as an alternativ... E. Cely Bonilla, C.L. Bazzi, R. Sobjak, K. Schenatto, E. Torres Avila, M. Rodrigues, S. Spricigo

269. Less Nitrogen, Same Yield: Evidence from 45 Site-Years of Sensor-Based Maize Nitrogen Management in the U.S. Midwest

Improving nitrogen use efficiency (NUE) while maintaining maize productivity remains a central challenge for irrigated corn systems in the U.S. Midwest. Sensor-based, in-season nitrogen (N) management has been around for many years. Yet, US Midwest farmers reported that a lack of information about the value of this approach and fear of yield loss when reducing the N rate were the top barriers to adoption. In-season N management enables better synchronization of N supply with crop demand, yet ... G. Balboa, P. Paccioretti, J.D. Luck

270. Machine Learning and Causal Analysis to Support Improved Crop Decision-making

While machine learning (ML) models, particularly Extreme Gradient Boosting (XGBoost) and Random Forest (RF), have demonstrated potential in generating accurate crop yield predictions, their practical adoption for on-farm decision support remains limited. A key challenge lies in their fundamentally associative nature, which, without additional tools, can reduce interpretability and diminish practitioner confidence. Explainable Artificial Intelligence (XAI) techniques like SHAP values address o... M. Chan Fu Wei, J.P. Molin, A. Colaço, L. Longchamps

271. Machine Learning Pipeline to Estimate Soybean Rust Severity Using UAV-derived Multispectral Indices

Asian Soybean Rust is one of the most destructive diseases affecting soybean crops worldwide and can result in yield losses of up to 90% when control measures are not implemented in a timely manner. Conventional disease monitoring based on field scouting is time-consuming, labor-intensive, and inherently subjective, often failing to adequately represent the spatial variability of disease across production fields. These limitations highlight the need for automated, objective, and high throughp... S.A. Teixeira, R. Valdivino, R. Tsukahara, M. Ribeiro

272. Machine Learning–driven Insights into Nitrogen Dynamics and Greenhouse Gas Emissions in Potato Production Systems

Nitrogen (N) is an essential nutrient for potato vegetative growth, yet it remains one of the most challenging elements to manage in modern agricultural systems. Despite its critical role in crop productivity, excessive or poorly timed N application can lead to significant environmental losses, particularly through groundwater nitrate (NO3-N) leaching and emissions of nitrous oxide (N2O), a greenhouse gas approximately 300 times more potent than CO2. Therefore... B. Javed, A. Cambouris, E. Smith, S. Dandrifosse, N. Ziadi, A. Karam

273. Main Environmental and Variety Drivers of Cotton Seed Quality: Historical Insights from the United States Cotton Belt 

Cotton seed quality traits including oil content, nitrogen (protein), and gossypol significantly influence seed value and end-use applications, yet their predictability based on environmental conditions across varied U.S. growing regions remains poorly understood. This study aimed to: (i) identify critical environmental predictors of seed composition; (ii) build machine learning models to predict seed quality as a function of seasonal weather patterns; and (iii) assess dif... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis

274. Making Sense of Unreplicated Farmer Experiments Through Causal Pathways

Most farmers experiment on their own farms every season, yet their conclusions are often drawn from limited data and are prone to misinterpretation. This study explores how scientists can support farmer-led on-farm experimentation (OFE) using analytical frameworks that complement — rather than disrupt — farmers' natural learning processes. The research evaluated a nitrogen-fixing inoculant (NFI) containing Klebsiella variicola and Kosakonia sacchari... L. Longchamps, P. Lanza, A. Yore, K. Rohrbaugh

275. Management Zone Delineation for Subsoiling Using Apparent Electrical Conductivity, Elevation, and Soil Moisture

Soil compaction is a primary physical factor limiting agricultural crop development. It mechanically impedes root growth, alters soil water dynamics, and consequently affects plant nutrient uptake. In agricultural systems under mechanization or animal trampling, compacted layers are spatially heterogeneous. This necessitates spatial analysis approaches for more precise management decisions, as average values often prove inadequate for site-specific problem resolution. Cons... T. Oliveira , G. Ramos Da Silva, E.A. Souza, R.M. Oliveira, B.F. Souza, M.A. Faria

276. Management Zone Delineation for the Optimization of Nitrogen Use Efficiency in Arabica Coffee Crops

Precision coffee farming requires efficient methods for Nitrogen (N) management—an input of high cost and environmental impact, whose optimization faces challenges due to the topographical characteristics of regions such as the Zona da Mata in Minas Gerais, Brazil. This study evaluates and compares different dimensionality reduction models for agricultural management zone (MZ) delineation, aiming to maximize nitrogen fertilizer use efficiency in Arabica coffee plantations. A dataset com... D.N. Nunes, R.P. Oliveira, L.D. Corrêdo, L. Peternelli, A.W. Pedrosa, V.H. Galvan, J. Souza

277. Management Zone Delineation Replacing Yield Maps with Vegetation Indices: Effects of Spatial Resolution and Machine Learning-based Selection

The delineation of Management Zones (MZs) is a precision agriculture strategy that exploits the spatial variability of crop fields to support more efficient and sustainable site-specific management practices. Traditionally, yield maps have been used as one of the main information layers in this process, as they integrate the effects of soil, climate, and management throughout the crop cycle. However, obtaining reliable yield maps still presents limitations, such as the need for onboard sensor... M. Gelain, J.P. Molin, L. De Goes Sterle

278. Manifold-Based Time-Lag Analysis of Soil Water Availability and Satellite Vegetation Indices for Irrigation Monitoring in Coffee Plantations

Monitoring soil water availability is essential for optimizing irrigation in perennial crops such as coffee, yet deploying dense in situ sensor networks remains impractical at scale. Although sensors like IGstat provide high-fidelity measurements of soil-water matric potential (SMP), their installation and maintenance costs limit broad adoption. A scalable alternative is to integrate sparse in situ observations with satellite-derived vegetation indices, including the Normalized Difference Moi... F. Johari, E. Ferreira, R. Prati

279. Mapping Digital Technologies, Cloud Platforms, and Artificial Intelligence in Precision Agriculture: The Software Baseline for a Citrus and Sugarcane Living Lab.

The digital transformation of Precision Agriculture (PA) has been driven by the growing availability of Farm Management Information Systems (FMIS), cloud platforms, and Artificial Intelligence (AI) solutions. This study, linked to the Smart B100 Science for Development Center (CCD-SB100), funded by FAPESP and led by the Agronomic Institute of Campinas (IAC), Faac/Unesp (Bauru), in partnership with FATEC Pompeia, aimed to build a multicriteria matrix (technological inventory) of digital PA sol... M. Mazega, H. Fortinis, H. Fischer, C.K. Luvizotto, C.E. Otoboni, M.C. De Almeida

280. Maturity Monitoring in Chickpea Using RGB Images Obtained by UAVs

Chickpea is a legume of great importance for global food security, and precise maturity monitoring is fundamental to optimize harvest timing and reduce grain losses. Remote sensing using unmanned aerial vehicles (UAVs) equipped with RGB cameras offers a non-destructive and high-throughput alternative for crop phenotyping, enabling rapid and reliable assessments of maturation progression. In this context, this study aimed to identify the best vegetation index based on RGB aerial images to moni... F. Soares, C.C. Santana, R. Avelar, F.O. Silva, A.L. Carvalho

281. Mobile Edge AI for Detection of Grape Clusters and Disease Symptoms in Vineyards

Precision viticulture demands accessible technological solutions that enable rapid disease diagnosis and production monitoring directly in the field. In real-world production contexts, dependence on cloud connectivity, external servers, or specialized hardware limits the adoption of computer vision tools by small and medium-sized farmers. In this context, this work presents a solution based on artificial intelligence embedded in a mobile application for the detection of grape bunches and leav... E.M. Da Silveira, F.I. Nogueira, S.D. Camargo, A. Freire Campos, J. Valiati, E.F. Leite

282. Mobile Robotic Soil Moisture Sensing and Mapping for Precision Agriculture in Small-Scale Farming

The spatial variability of soil moisture is a key factor for efficient irrigation management and agricultural productivity, particularly in small-scale farming systems where access to precision agriculture technologies remains limited due to cost and operational complexity. This study presents the development and preliminary evaluation of a robotic sensing system for georeferenced soil moisture data acquisition, focusing on spatial mapping in small agricultural areas. The proposed s... D.H. Hall, M. Hosser, R.L. Machado, R. Galli, A.L. Machado

283. Monitoring the Invasive Grass Eragrostis plana with Artificial Intelligence: A Comparative Study of Hyperspectral Data and Drone-Based Object Detection

The invasion of exotic plant species is recognized as one of the major threats to biodiversity and ecosystem stability worldwide. In the Brazilian Pampa biome, Eragrostis plana Nees (commonly known as Annoni grass) has become one of the most aggressive invasive species since its introduction in the 1950s. Currently occupying approximately 20% of the native grassland vegetation in the state of Rio Grande do Sul, this species exhibits high adaptive capacity, rapid propagation, and the absence o... S. Camargo, N. Perez, T.S. Lopes, A.R. Silveira

284. Monitoring, Automation, and Control System for Small Scale Silos

Brazil has an important role in the global grain production scenario. However, the country’s grain storage infrastructure is usually inadequate and insufficient. The drying and storage of grains post-harvest are essential to ensure product quality, even over extended periods. In this context, research has been conducted on sensing, monitoring, and prediction of grain temperature and humidity, as well as the automation and control of the drying process. Therefore, this paper presents a s... C.C. Scharlau, C.R. Krumreich, N. Pagani Neto, H.M. Tamba , B. Culda

285. Multi-Band UAV-Borne SAR Sensitivity (C, L, and P Bands) for Detecting Leaf-Cutting Ant Nests in Eucalyptus Plantations

Planted forests in Brazil cover approximately 10.5 million hectares and are recognized worldwide for sustainable management and the supply of bioproducts derived from renewable raw materials. In addition, the country stands out in pulp production and exports, ranking second only to the United States. However, the planted forest sector has faced phytosanitary challenges, particularly related to leaf-cutting ants, which cause biomass losses and reduce leaf area, compromising photosynthetic capa... W. Batista Da Silva , A. Santos, T. Costa Barboza, O.P. Costa, G. Valdes Fernandez , M. Ciscato, G. . Silveira, R. . Filho

286. Multivariate Analysis of Structural, Climatic, and Management Factors Associated with Cotton Yield in the Brazilian Midwest

Understanding the integrated drivers of cotton yield in highly intensified production systems remains a central challenge for precision agriculture. This study aimed to assess, from a systemic perspective, the structural, climatic, phytosanitary, and management factors associated with cotton yield during the 2024/25 season in large-scale commercial farms located in the states of Mato Grosso and Mato Grosso do Sul, Brazil. Approximately 91,000 ha were analyzed across multiple farms and... R. Rimoldi Tavanti, G. Morais, D.

287. NDVI Index and Its Correlation with Biennial Coffee Yield

Precision agriculture (PA) has consolidated itself as an important tool in agricultural management, allowing greater productive efficiency, cost reduction, and environmental sustainability. Among PA tools, the use of vegetation indices stands out, as it allows inferences about crop biomass both spatially and temporally. In this context, this study aimed to evaluate the relationship between the normalized difference vegetation index (NDVI) and the yield of Arabica coffee (Coffea ... L.V. Lazzarini, S.M. Hurtado, I.D. Gonçalves, M.F. Carneiro Filho, A. , G.P. Cândido

288. Non-destructive Detection of Herbicide Damage in Curly Lettuce Using Spectral Data and Machine Learning Algorithms

Curly lettuce (Lactuca sativa var. crispa) is a prominent horticultural crop due to its high demand for both production and human consumption. It plays a vital role in creating healthier, more balanced diets. However, the application of phytosanitary products, such as herbicides, whether applied by air or land, can lead to chemical drift into adjacent areas, negatively impacting sensitive crops. This drift can cause phytotoxicity and, in severe cases, result in total crop loss, depending on f... F. Morlin Carneiro, T. Vidigal Maciel, G. Albuquerque Araujo, H. De Oliveira Cavalheiro, B. Matwijou, M.A. Da Silva, A. Lopes De Brito Filho, M.G. Da Silva Brochado, J.D. Rodrigues Oliveira

289. Nonlinear Modeling of Vegetation Response to Rainfall Variability in the Brazilian Semi-Arid Region Using Sentinel-2 and CHIRPS Data

High climate variability in the Brazilian semi-arid region poses significant challenges to agriculture and the sustainable management of Caatinga ecosystems, requiring monitoring tools capable of anticipating vegetation responses to rainfall fluctuations. However, the spectral response of vegetation to precipitation does not always follow linear patterns and may reflect ecohydrological thresholds and water saturation effects. Sentinel-2 data were used to derive the Soil Adjusted Vegetation In... A. Fonseca, E.F. Da Silva, A.C. Bezerra, G.B. Moura, J.F. Dos Anjos, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, J.I. Silva

290. North America Regional Meeting

... A. Cambouris

291. On-Farm Application of Variable Rate Irrigation Strategies

It is estimated that nearly 70% of the world’s freshwater withdrawals are used for agriculture. As water resources continue to decline, agricultural systems face increasing pressure to maximize productivity to meet growing global food demand while ensuring sustainable water use. Addressing water management challenges is therefore critical. Variable rate irrigation (VRI) offers a promising solution to improve irrigation use efficiency by adjusting water application rates according to the...

292. On-Farm Experimentation Community Meeting

... L. Longchamps, S. Cook

293. On-farm Precision Experimentation in Western Canada: A Case Study Focused on Adoption, Challenges, and Practical Results

On-farm experimentation can improve management practices by providing localized results that align with the environment, genetics, and management of a specific field, farm, or region. However, operational challenges, such as the time required to implement and harvest an experiment and analyze results, can be barriers to adoption. The technology available in modern machinery (e.g., variable-rate technology and yield monitors) can overcome operational challenges by autonomously implementing and... F. Hoffmann Silva Karp, B. Bateman, H. Simons, J. Boychyn

294. Opening Ceremony: Special Guest, ISPA & AsBraAP Presidents, and Brazilian Agriculture

... M. Albuquerque

295. Operational Satellite Weed Detection Across 14,000 Sugarcane Fields: Lessons in Temporal Feature Design

Weed infestations in sugarcane (Saccharum officinarum L.) can reduce yields by 20–60% depending on species composition and management timing, yet operational weed management at scale remains an unsolved challenge. Existing studies typically cover tens of fields; scaling to thousands introduces challenges in processing throughput, ground truth scarcity, and feature design. This work describes the development and operational deployment of a satellite-based weed detection system ... C. Ferraz, R. Fortes, M. Cabrera Dengra, J. Poli, E. Bernardes Júnior, A. Do Vale Dondo

296. Optical Chlorophyll Sensor in the Identification of Coffee Cultivars Adapted to the Pitangui-MG Region

The agronomic performance of coffee plants is directly related to the interaction between genotype and environment, making it essential to identify cultivars best adapted to specific growing conditions. In recent years, the incorporation of tools in agriculture has significantly improved the plant evaluation process. Among these technologies, portable sensors, such as the chlorophyll meter, allow for rapid, non-destructive, and highly sensitive measurements of physiological parameters related... A.D. Freitas, C.C. Santana, F. Silva, R. Avelar, T.A. Rodrigues

297. Optimal Hyperspectral Band Selection for UAV-Based Aflatoxin Risk Prediction in Peanut Field

South Georgia’s humid subtropical climate and well-drained sandy soils make Georgia the leading peanut (Arachis hypogaea L.) producing region in the United States, accounting for more than half of the nation's production. However, aflatoxin contamination caused by Aspergillus fungi remains a significant concern, posing serious food safety risks and causing substantial economic losses. Rising temperatures and humidity from the climate crisis create ideal conditions for fungi... N. Niva, L. Lacerda, G. Vellidis, S. Maktabi, M. Ardigueri

298. Optimization of Electrochemical Device Development: Laser-Induced Graphene Electrode as an Alternative for Agricultural Monitoring.

The agro­industrial sector has driven the technological development of electrochemical devices aimed at field applications. In this context, laser-induced graphene (LIG) electrodes stand out for enabling electrode miniaturization, favoring in situ analyses and equipment portability. These devices exhibit high sensitivity, selectivity, rapid response, and low cost, characteristics that expand their application potential in different scenarios. However, the growing demand for these devices ... C. Miler, L. Gonçalves, B. Vasconcellos Lopes, B.B. Gallo, A. La Rosa, D. Fruchtenicht, P. Silveira, F. , N. Carreño, L. Machado

299. Optimization of Flight Parameters for High-Throughput Phenotyping of Guineagrass Using UAS

A fenotipagem de alto rendimento utilizando sistemas aéreos não tripulados (UAS) tornou-se uma ferramenta estratégica na agricultura de precisão aplicada a pastagens, permitindo a coleta rápida e não destrutiva de dados em larga escala. No entanto, a definição adequada dos parâmetros de voo, especialmente a distância de amostragem do solo (GSD) e a sobreposição de imagens, permanece um desafio, visto que configu...

300. Orchestration of Missions for Coordination between Autonomous Agents in Agriculture.

Due to technological advancement in agriculture, various autonomous agents, such as drones, mobile robots, and intelligent agricultural vehicles, are being used to automate repetitive tasks and increase agricultural production. In addition, these agents, often developed by different manufacturers and endowed with different capabilities, form a highly heterogeneous environment, imposing a central challenge to be solved: the need to manage these agents so that they can act in a coordinated and ... V. Fontena, N.K. Wagner, C. Teixeira, J. Lopes, L. Moura, C. Guimarães

301. Panel Discussion - Precision Ag in Brazil

... F.R. Martins, A. Pavinato

302. Panel: Developing Enabling Technologies for Digital Agriculture – International Perspective

... K. Schilling, F. Soares, I. Small, S. Krco, C. Eduardo Pereira, T. Santos

303. Panelist Question and Answers

... J.P. Molin, D. Cammarano, S. Virk, B.V. Ortiz

304. Pedometrics Innovations and Applications for Precision Agriculture

... R. Roberto Poppiel

305. Performance of Autonomous Navigation in an Agricultural Tractor Using Pure Pursuit Control and Dubins Path Planning

Autopilot systems in agricultural machinery are primarily designed to ensure accurate tracking of predefined routes. However, their performance is strongly influenced by the internal parameters of the control algorithms employed, which may vary according to operational conditions. In this context, computational simulations constitute a valuable tool for investigating these interactions and identifying optimal operating configurations. This study evaluates an autopilot system based on the Pure... J.D. Baltazar, A.L. Coelho, L. De Arruda Viana, A.D. Brandão, D.S. Valente, M.R. Furtado Jr

306. Performance of Horizontal Seed Metering Technologies for Maize at Different Angular Velocities

Brazil is a global leader in grain production, with an estimated record of 353.1 million tons for the 2025/26 harvest. Maize cultivation showed an increase in the total estimated sown area, totaling 22.7 million hectares across the three harvests, with a 4% growth expectation—rising from 21.7 million hectares in 2024/25 to 22.8 million hectares in the current season, which corresponds to an increase of 871,800 hectares. The demand for maize grain is steadily growing for both animal feed... J. Santos, V. Kaster Marini

307. Performance of Spatial Prediction Models Under Different Sample Densities in Corn Yield Maps

Yield maps are essential for the consistent management of crop variability. The accuracy of these maps, however, is directly affected by the density of data collected by grain harvesters and by the choice of interpolation method, whether deterministic or statistical. This study aimed to evaluate the performance of four spatial prediction methods, tested under different sample densities, for the development of corn yield maps. The study area corresponds to an 11-hectare commercial field, utili... L.M. Gimenez, L.M. Santos, M. Hass Bomfim Vieira

308. Performance of Unmanned Aerial Vehicles to Broadcast-interseed Cover Crops at Different Crop Stages

Drones have become more affordable, making them a viable option to use for broadcast-interseeding cover crops within crops prior to harvest. This strategy has become popular in the USA over the past three years. However, limited information exists on spreading with drones and the in-field performance and setups to ensure uniform distribution.  Therefore, the objective of this study was to assess the distribution uniformity of broadcast- interseeded cover crops into cash crops prior to ha... J.P. Fulton, A. Thomas, S.A. Shearer, E. Hawkins, S. Khanal

309. Plant-Level Coffee Production Estimation Based on Morphological Indices

Production estimation in coffee farming is traditionally conducted at aggregated spatial scales, which often limits the characterization of variability among individual plants and constrains its applicability for precision-oriented management. In production systems where within-field heterogeneity affects decisions related to harvesting, logistics, and crop management, approaches capable of representing plant-level variability become particularly relevant. Within this context, this study prop... D. Queiroz, D.H. Leite, D. Sárvio Valente, G. Dumbá Monteiro De Castro, D.B. Marin

310. Plot2Phenome: A UAV-Based Deep Learning Framework for Automated Micro-Plot Segmentation and Plot Level Phenotyping

Automated micro-plot segmentation is a foundational requirement for plot-level phenotyping from UAV orthomosaics in field breeding trials. However, reliable delineation of individual plots remains difficult in realistic agronomic settings, particularly under canopy closure that erodes inter-plot gaps and under irregular or degraded plot boundaries caused by lodging, variable emergence, and field operations. These conditions reduce boundary contrast, increase instance adjacency, and introduce ... Y. Li, C. Jin, X. Zhang

311. PRAGMATIC - Innovative IT Platform for Yield and Cost Prediction of Agricultural Production

The  aim  of  the  R&D  was  to  develop  a  prototype  of  an  innovative  IT  platform  containing algorithms  for  predicting  yields  and  production  costs  of  agricultural  commodities  for  three reference crops, i.e.: blueberries, apples and potatoes in the supply chain from the field to the production  line.  The  system  are ... T. Wojciechowski, G. Niedbała, K. Bobran

312. Precisely Monitoring Nitrogen Requirements for Winter Wheat and Spring Barley Based on Crop Yield Predictions, Remote Sensing Imagery and Soil Texture Maps

The timely and precise evaluation of crop nitrogen demand is crucial for maximizing farmers' contribution margin while simultaneously minimizing nitrogen fertilization. Sufficient nitrogen fertilizer has to be provided for adequate crop growth, yet fertilization should not be excessive to ensure its environmental impact is minimized. A variety of factors determine nitrogen demands as well as crop yield, including weather, topography and soil texture. These factors vary spatially, meaning ... N. Hollain, S. Wang, B. Feld Mikkelsen,

313. Precision Agriculture in Cocoa Production: Evaluating RGB-Derived GNDVI as a Low-Cost Multispectral Proxy in the Brazilian Amazon

Cocoa production in the Xingu region of Pará, Brazil, represents the country’s main production hub, accounting for more than 76% of the state’s total output. Despite its economic importance, cocoa management remains largely traditional, with limited adoption of Precision Agriculture (PA) technologies due to the high cost of multispectral sensors and restricted technological access among growers.This study evaluated the seasonal compatibility between mean visible-spectrum re... R.D. Santos, C. Silva E Silva, N. Cornejo Noronha, C. Tanajura Caldeira, . Soares Cardoso, W. De Pinho Alvarez

314. Precision Irrigation and Beyond: A Multi-Step Zoning Approach for Vineyard Water Management and Wine Quality Enhancement

Spatial variability in soil moisture, terrain, and vine physiology presents a major challenge for efficient water management in vineyards. Traditional uniform irrigation often fails to address this heterogeneity, limiting both water use efficiency and wine quality. This study investigated spatial and temporal variations in vine water stress over five consecutive growing seasons, and evaluated zone-specific precision agriculture strategies to support improved irrigation and complementary viney... Y. Cohen, I. Bahat, J.M. Grünzweig, V. Alchanatis , O. Keisar, G. Lidor, E. Goldshtein, Y. Netzer

315. Precision Phenotyping for Yield Prediction in Soybean

In the contemporary landscape of Precision Agriculture 4.0, the rapid and non-destructive quantification of plant structural traits is a cornerstone for accelerating soybean breeding programs and optimizing field-level cultivation strategies. Traditional manual phenotyping methodologies are inherently limited by high labor intensity, significant subjectivity, and low throughput, which hinder the ability to analyze large populations during critical reproductive stages. To bridge this gap,... W. Su

316. Precision Tillage Operations: Analyzing the Efficiency of Conventional and Robotic Systems

ABSTRACT. The crop production sector is labor- and energy-intensive, significantly impacting the environment. Soil tillage is one of the most expensive and polluting technological operations; therefore, modern automated and precision technologies applied according to soil variability can help change economic costs and environmental pollution. This study evaluated the effects of site-specific variable depth tillage using two combinations of a conventional tractor and a multifunctional cultivat... E. Šarauskis, S. Sokas, I. Bručienė, S. Buragienė, M. Kazlauskas, V. Naujokienė

317. Predicting Maize Physiological Traits from Multispectral UAV Imagery Using Machine Learning Algorithms

Maize has major global importance for human and animal nutrition. The identification of physiological parameters is an essential tool for decision-making in crop management. When associated with these parameters, machine learning (ML) enables the analysis of large volumes of data, making it a suitable approach for robust datasets. Therefore, this study aimed to estimate physiological parameters correlated with vegetation indices through the application of ML models across different field area... E. Amaral, T. Costa Barboza, M. Ardigueri, U. Sigdel, L. Lacerda, A. Felipe Dos Santos

318. Predicting Peanut Maturity and Yield by Integrating Multicriteria Regression and Remote Sensing.

Accurate predictions of yield and maturity are essential for optimizing crop management in low-tech crops such as peanuts. However, the spatial and temporal variability of these variables poses significant challenges for conventional methods. Yield can be determined using labor-intensive manual methods or sensors on harvesters. Despite advances, there is a scarcity of studies that simultaneously integrate yield and maturity prediction for peanuts, a critical step for harvest optimization. The... J. Souza, J. Lucas Da Silva Ferreira , S. Luns, M.F. Oliveira, W. Sousa

319. Predicting Pre-harvest Cotton Fiber Quality: An Open-data and Machine Learning Framework

Intra-field variability in soil properties and topography, and inter-field variability in weather patterns often leads to inconsistent cotton fiber quality and yield outcomes, posing challenges for growers. This study aims to: (i) predict within-field cotton fiber quality traits based on environmental and soil variables using machine learning models; (ii) identify the most influential environmental drivers (weather, vegetation indices, soil properties, terrain characteristics) affecting cotto...

320. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor Data

Site-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton produ... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal

321. Prediction of Leaf Nitrogen in Corn Cultivated Under Organomineral Fertilization Using Machine Learning and Multispectral Aerial Imagery

One of the main determining factors of corn productivity is nitrogen (N) fertilization. However, significant knowledge gaps still persist regarding plant responses to top-dressing nitrogen application with organomineral fertilizers. Conventional methods for assessing nutritional status, such as leaf tissue analysis and portable chlorophyll meters, although efficient, are limited by their point-in-time nature, failing to adequately represent the spatial and temporal variability throughout the ... E. Sousa Meneses, M. Antônio, A. Batista Santos, L.C. E. Ribeiro, C.C. Santana

322. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery

Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluat... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana

323. Prediction of Soil Potassium Concentration Using Electrical Impedance Spectroscopy

The increasing global dependence on potassium fertilizers and instabilities in international trade, intensified by recent geopolitical conflicts, have raised concerns regarding potassium (K) availability and costs in 2026. Since variable-rate fertilizer application enables the optimization of agricultural inputs, the delineation of management zones based on the spatial variability of soil potassium concentration becomes essential. This study aimed to evaluate the use of electrical impedance s... I. Salvador, A.L. Coelho, S.V. Valarares, D. Queiroz

324. Predictive Analysis of Fertilizer Efficiency with Machine Learning

Fertilizers play a key role in agribusiness, both as an essential input for agricultural productivity and as a strategic component in the commercial chain. They provide nutrients that are indispensable for soil correction and crop growth, such as nitrogen, phosphorus, and potassium, allowing the soil to maintain its capacity to sustain crops even after several harvests. It is estimated that about 50% of global food production depends on the use of fertilizers, and in Brazil, these inputs repr... C.S. Santos, R.K. Weber

325. Probfuse Dashboard: Uncertainty-aware Geospatial Fusion For Climate-smart Conservation Recommendations In The Maumee River Basin

Nutrient losses from tile-drained row crops in the Maumee River Basin remain a primary driver of harmful algal blooms in western Lake Erie, despite expanding conservation programs and cost-share incentives like the Environmental Quality Incentives Program (EQIP). Existing tools rely on static look-up tables or county averages, lacking probabilistic fusion of multi-source data or uncertainty estimates. This hinders field staff and producers from integrating soils, climate and program rules und... H. Subramoni, A. Murumkar, K. Ard, S.A. Shearer, A. Radhakrishnan, J.P. Fulton, K. Mundada

326. Producing Ordinary Kriging Interpolated Maps for Biomass Observation Through Values Captured with NDVI and NDRE Imagery.

Geostatistics is a well-established method in the scientific community for aiding decision-making in situations with spatial dependence. Generally, the methodology adopted for interpolating fertility maps is the use of data from soil sampling on the property, generating representative thematic maps. However, the number of samples required for this methodology can be problematic when the analysis site is a small farm or one divided into multiple plots, common scenarios in Brazilian coffee farm... H.F. Gebler, C.R. Grego, G.C. Rodrigues, A. Pereira, F. Fagundes

327. Proprietary vs. Open-Source Visual-Inertial Fusion Under GNSS Degradation for Orchard-Scale 3D Fruit Mapping

A recent pipeline combining GNSS-visual-inertial odometry with factor-graph refinement of fruit landmarks has reported orchard-level apple counting errors below three percent against harvest totals — a result with few precedents in the agricultural SLAM literature, where GNSS-VIO fusion, landmark-level optimization, and harvest-validated yield estimation have until now appeared only in isolation. However, trajectory quality in that pipeline was ... T. Santos, D. Bharti, L. Gebler, A. De Rossi

328. Proximal and suborbital vegetation indices in yield prediction of ‘Syrah’ grapevines

The various vegetation indices (VIs) reported in the literature, derived from different wavelengths, necessitate identifying the most suitable spectral combinations to represent agronomic variables in precision viticulture. This study evaluated the performance of proximal and suborbital VIs to explain the spatial variability of yield of the ‘Syrah’ grapevine. The study was conducted in a trellised vineyard under double pruning management in Ribeirão Preto, state of Sã... L.H. Bassoi, L.A. Jorge, A. Pereira, I. Oliveira Junior

329. PTx TRIMBLE - Far Beyond the Map: The Power of Split-Second Decision-Making in the Cockpit

... G. Coscelli Rocco

330. QGIS Applied to Precision Agriculture: Mapping and Spatial Analysis of Agricultural Areas

This course presents the fundamentals and applications of QGIS in precision agriculture, focusing on mapping and spatial analysis of agricultural areas. Participants will learn how to use GIS tools to organize, visualize, and interpret geographic data for agricultural planning and management. Topics include geoprocessing basics, data manipulation, thematic mapping, and spatial analysis for crop diagnosis and monitoring.   Target audience: Und... V.R. De Novais

331. Quality of Interpolated Maps of Soil Mechanical Resistance to Penetration as Support for Variable Rate Compaction Management

Reliable continuous maps of soil mechanical penetration resistance (SMPR) are essential for site‑specific compaction management, but their usefulness depends on how interpolation methods respond to different spatial dependence patterns along the soil profile. Rather than seeking a single “best” interpolator, this study explicitly addresses how the suitability of interpolation methods varies as a function of the spatial structure of SMPR, an aspect seldom explored in depth in pre... R.P. Silva, J.D. Riquiel, A. Andrade Da Silva, E. Sales, T.M. Oliveira, R. De Souza Silva

332. Quantifying Prediction Uncertainty in Field-scale Soil Maps Generated by Machine Learning

.Field-scale maps of soil properties are a key component of precision agriculture, as they are routinely used as inputs for variable-rate fertilization, zone delineation, and site-specific management. While machine learning models have substantially improved the accuracy of spatial predictions, uncertainty associated with these predictions is often ignored, limiting the reliability of soil maps as decision-support tools. Quantifying prediction uncertainty is essential not only to assess map q... F. García Seleme, P. Paccioretti, M. Balzarini, M. Córdoba

333. Quantitative Analysis of Sub-catchment Scale Erosion-sedimentation Dynamics in a Grassed Buffer Zone Using Hyper-resolution Soil Loss Modeling in a Hungarian Hilly Arable Environment

Soil erosion represents one of the most severe environmental and economic risks in arable crop production, leading to the irreversible degradation of the topsoil layer. This research aims to provide a quantitative analysis of the sediment retention capacity of a grassed buffer strip installed within an erosion rill, utilizing 1*1 m resolution soil loss modeling. The methodological base of the study was the Unit Stream Power-based Erosion Deposition (USPED) model. The application of ... Z. Szenek, D. Szám, C. Centeri, G. Milics, M. László

334. RAVI: A QGIS plugin for satellite remote sensing applications of Vegetation Indices and SAR data in Precision Agriculture

Remote Sensing (RS) plays a fundamental role in Precision Agriculture (PA), particularly through the use of satellite imagery to identify spatial variability within the fields. Compared to traditional methods for detecting field variability, such as soil sampling, yield mapping, and proximal sensors, RS offers advantages in reduced operational costs, lower labor demands, and greater spatial coverage. Analyzing vegetation indices (VIs) over time allows to track crop phenological development, i...

335. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient Attributes

Soil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, alth... A. Ten Caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva

336. Relationship Between Soil Classes and Grape Yield in a Vineyard of the Campanha Gaúcha Region

Brazilian viticulture has shown significant expansion in recent decades. However, this productive growth has brought new challenges for vineyard management, particularly regarding the understanding of soil spatial variability and its relationship with grape yield and quality. The objective of this study was to correlate the spatial variability of soil classes with grape yield. The study was conducted in a commercial vineyard located in Santana do Livramento, in the Campanha Gaúcha regi... B. Baumgardt, B. Trevizan Paese, J.M. Moura Bueno, G. Brunetto, A.A. Kokkonen, A. Benetti

337. Relationship Between Temporal Variability of Soybean Yield and Stable Soil Attributes

Management zones are widely used in precision agriculture and can be defined by different factors; however, uncertainties remain regarding their temporal stability when based on a single soil attribute. This study aimed to analyze the relationship between a temporal series of yield from five agricultural fields and four stable soil attributes—clay content, soil organic matter (SOM), Topographic Wetness Index (TWI), and apparent electrical conductivity (ECa)—using multiple linear r... G. Kaefer Seganfredo, L.G. Kern, L. Silveira Pavão, A. Müllich, I. Maldaner, J. Sgarbossa, L. , E. Rolim Farias Da Silva, M.S. Farias

338. Remote Sensing for Identification of Soil Texture Variability in Precision Agriculture

Understanding spatial variability of soil texture is essential for site-specific management in Precision Agriculture. Traditional approaches rely on intensive soil sampling and apparent electrical conductivity (ECa) surveys, which provide high-quality information but may be costly and difficult to scale. Satellite remote sensing offers a promising alternative by enabling indirect estimation of soil properties through spectral responses. This study evaluated the potential of Sentinel-2 spectra... T. , L.R. Amaral

339. Remote Sensing for Identifying Soybean Cultivars and Estimating Crop Yields

Traditional methods for cultivar identification and agricultural productivity estimation have been increasingly complemented or replaced by innovative approaches using geotechnologies and Artificial Intelligence (AI). These modern techniques offer greater efficiency, speed, and sustainability in agricultural production systems. Among these advances, Remote Sensing (RS) has stood out as an effective tool for agricultural monitoring. It allows the collection of spectral and biophysical informat... B. Matwijou, J.R. Oliveira, M. Da Silva, G.G. Scheidt, A. Lopes De Brito Filho, M.G. Da Silva Brochado, F. Morlin Carneiro

340. Remote Sensor Estimation of Cover Crops Macronutrients Uptake Aiming to Support Farmers’ Fields Nutrients Management.

The introduction of Cover crops/Catch crops into the Cash crops field rotation system is increasing year after year and becoming common practice among farmers. In Europe, this is especially driven by Regenerative agriculture practices and government subsidies. But the introduction of cover crops changes the dynamics of nutrients in the field cultivation system, as legumes cover crops can biologically fix atmospheric nitrogen to the soil, bring nutrients from the sub-soil to the surface, t... G. Portz, C.M. Mboh, M. Gnyp, I. Kyere

341. Results from a Scoping Review: the Role of Autonomous Mechanical Weeding Robots in Climate-smart Soil Management

The growing demand for sustainable agricultural practices has driven advancements in digital agricultural technologies, which is also reflected in the emerging development and market release of agricultural field robots in the last decade. Climate-smart sustainable soil management plays a key role in sustaining soil functions related to productivity, water and nutrient cycling, biodiversity and long-term resilience. The integration of autonomous field robots, for which mechanical weeding is c... K. Grahmann, L. Rohlmann, L. Thielemann, A. Roy, C. Weltzien

342. Row-unit Integrated Multi-camera Edge AI System for Real-time Small-grain Seeding Performance Data Collection

High-quality synchronized imagery collected under field conditions is a limiting factor in the development of computer vision models for small-grain seeding applications. This study presents the design, implementation, and field deployment of a row-unit integrated multi-camera data-acquisition system intended to standardize multi-view data collection during planting. The system mounts directly to a seed-drill row unit and integrates three Power-over-Ethernet (PoE) Basler cameras positioned to... A. Sharda, B. Vail, S. Rai, R. Harsha Chepally

343. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI

Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite ... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes

344. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in Agriculture

Satellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agr... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi

345. Satellite-IoT Integration for Digital Agriculture: Advances and Challenges

Agriculture is an essential pillar of global food security, demanding continuous digital integration from seed selection and planting to post-harvest logistics and crop distribution. In this scenario, seamless communication is the primary enabler for data-driven decision-making, ensuring that critical information regarding environmental variables, soil moisture, and direct plant health indicators reaches processing platforms fast enough for agile operational responses. However, the expansion ... M. Feldman, O.R. Da Cruz, N.K. Wagner, C. Glier, I. Müller

346. SAVE FARM EIRENE - Computational Vision in the Field: How AI is Transforming Spraying

... E.

347. Scalable Offline Infrastructure for Agriculture 4.0: A Linux-Gateway Architecture for Edge Connectivity and Data Sovereignty

Brazilian agribusiness has consolidated itself as an economic pillar, representing approximately 25% of the national GDP. However, the full rise of Agriculture 4.0 - characterized by the integration of IoT, Artificial Intelligence, and smart sensors -faces a scenario of profound technological inequality. Recent data indicate a disparate adoption of digital tools: while large-scale farms possess the financial capacity for complex infrastructures, small and medium-sized producers face connectiv... M. Andrade, C. Soares De Souza

348. Seeing What Sampling Misses: Integrating High-Resolution pH, Compaction, and EC Sensing for Improved Soil Management

Traditional laboratory soil sampling remains foundational for nutrient measurement and provides reliable chemical analysis at the point of collection. However, the combined cost of laboratory analysis and labor-intensive sample collection often constrains sampling density, resulting in maps that are accurate at discrete locations but interpolated across large unsampled areas. In addition, laboratory testing primarily characterizes chemical properties and does not often measure physical soil c... E. Lund, C.R. Maxton, T. Lund

349. Segmentation of Morphological Structures of Soybean Seedlings Using Convolutional Neural Networks

Soybean is one of the most relevant and highly demanded agricultural commodities worldwide, playing a central role in the production chains of food, animal feed, and vegetable oil. Globally, there is a growing demand for food, which has driven strategies to meet the needs of the world population. Many of these strategies involve expanding arable land and excessively exploiting soil nutrients in an unsustainable manner. An alternative strategy to address this issue is to incorporate seed lot q... P. Dos Santos E Silva, J. Martins Neto, E. Freitas, D. G. Gomes, H.F. Abud

350. Selection of UAV-based Vegetation Indices for the Prediction of Leaf Chlorophyll Content in Maize Using a Normalized Partial Least Squares Regression (PLSR) Reduction Approach

The accurate monitoring of the nutritional status is essential for optimizing nitrogen (N) fertilization and maximizing maize grain yield. Variations in N availability directly affect agronomic parameters such as leaf chlorophyll content, which can be estimated using optical sensors. This study assessed the effects of urease inhibitors and nitrogen application rates on leaf chlorophyll content and predicted total leaf chlorophyll content in maize using relevant vegetation indices under field ... B. Nogueira, E. Bender, D. De Carvalho Arruda, M. Da Costa Salem, L. Espindola Muller, S.R. Dos Santos Gonçalves Junior, G. Eissmann Souza, J.V. Muller Klassmann, B.B. Gallo, C. Bredemeier

351. Semantic Segmentation Comparison of Prata Catarina Banana Bunches Using Convolutional Neural Network Models

The identification and classification of banana ripening stages are essential for production assessment in large scale plantations, enabling efficient harvest monitoring and ensuring fruit quality for commercialization. This study presents a comparative evaluation of three deep learning architectures applied to the semantic segmentation of Prata Catarina banana bunches, aiming to support automated monitoring systems and decision-making tools for precision agriculture applications. The evaluat... J. Rodrigues Moreno, E.L. Silva, E. Freitas, D. G. Gomes, Y. Costa G. Da Silva

352. Semi-Automatic Plot Segmentation for Crop Phenotyping Using Adaptive Spectral Indices and SAM3

Yield trials and hill plots are widely used in plant breeding to evaluate large numbers of genotypes simultaneously. Extracting per-plot canopy boundaries from drone imagery is a key step in this process, but manual delineation is time-consuming, and rigid grid overlays do not account for true canopy boundaries. This paper presents an annotation-free pipeline for segmenting individual plots from multispectral drone imagery, requiring only approximate plot dimensions as input. The pipeline fir...

353. SENAI - Holoagro - Intelligent Agricultural Supply Chain Control

... F. Rocha De Avila

354. Sensing-Based Correlation Analysis of Surface Elevation and Topsoil Depth in Japanese Rice Paddy Field

Proper management of soil physical properties is fundamental for stabilizing crop yields and optimizing resource efficiency in large-scale rice paddy production. Among these properties, field surface elevation and topsoil depth (TD) are critical determinants of water management effectiveness and root zone environments. This study conducted a high-resolution grid-based correlation analysis between surface elevation and TD in a 1.4-ha paddy field in Tottori Prefecture, Japan. To overcome the li... E. Morimoto, J. Lee, K. Nishida, H. Itoh

355. Sensor Fusion and Fitness-for-purpose Screening for SOC Prediction in Vis–NIR Spectral Libraries, Insights from Vis–NIR Plus XRF, Local Calibration, and MRV Thresholds

Soil organic carbon (SOC) monitoring requires analytical tools that are accurate and scalable for measurement, reporting and verification process (MRV). In practice, MRV programs also require transparent error interpretation so that sensor-based inference can be linked to decision risk under different field conditions. We assessed whether fusing X-ray fluorescence (XRF) information with vis–NIR spectra improves SOC prediction when models are trained on large spectral libraries and trans... T. Tavares, L.A. Da Silva, E. , M.R. Cherubin

356. Sensor-based Optimal Delineation of Management Zones for Plant and Soil Integration

Strategies for mapping management zones (MZs) use proximal and orbital sensors to optimize use and promote sustainable development. Proximal sensors can infer, among other variables, apparent electrical conductivity (ECa), while orbital sensors provide synthetic soil images (SYSI), elevation, and different color-composition images of soil and plant canopies. However, the literature does not provide clear evidence on the efficiency of these measurements, individually and jointly, in reducing t... A.V. Hereman, J.V. Pozzuto, L.R. Amaral

357. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution

Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield di... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos

358. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in Corn

Nitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Hor... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal

359. Silage Corn Production Under Different Management Strategies: Conventional and 4.0

Agriculture 4.0 has emerged as a strategic tool to maximize operational efficiency and environmental sustainability in agricultural production. The integration of telemetry, automation, and spatial data analysis facilitates more precise management, reducing input waste and enhancing production predictability relative to traditional methods. In this context, the objective of this study was to evaluate the impact of adopting Agriculture 4.0 technologies on the agronomic performance and producti... F. Aguiar Jordão, D.J. Santos, G.D. Dalevedo, L.A. Gaion, I.M. Pascoaloto, E. Fernandes, J. , T.F. Lemos

360. Simplifying Lab Analysis for Mapping Texture and Om Content Via Sensor-based Inference: a Case Study Showing Maps of Eca and Traditional Methods

Precision agriculture hinges on soil information at spatial resolutions that capture within-field variability, yet conventional laboratory workflows often constrain sampling density due to cost and turnaround time. In this study, we evaluated a laboratory-based sensor inference service for mapping soil organic matter (OM) and texture (clay and sand) using visible and near-infrared spectroscopy (vis-NIR) and X-ray fluorescence (XRF) spectroscopy. We further evaluated whether resulting maps are... -. -, E. Casciello, J. Pozzuto, T. Tavares, L. Da Silva, H.W. De Carvalho

361. Simulation of Different Nitrogen Fertilization Strategies Using Management Zones in Sugarcane Cultivation

Nitrogen plays a central role in sugarcane physiology, as it is a structural component of amino acids, proteins, nucleic acids, and chlorophyll, being essential for photosynthetic activity, leaf expansion, biomass accumulation, and stalk formation. Given its relevance, nitrogen management efficiency represents a central research topic in sugarcane, particularly in systems characterized by strong spatial heterogeneity, where soil physical–hydric attributes, fertility levels, and environm... G.V. Bedum, J.P. Molin, R. Canal Filho

362. Site-specific Nutrient Management in Citrus: Agronomic, Economic and Energy Implications of Variable Rate Fertilization

Brazilian citrus production faces increasing challenges due to rising costs, intensifying climatic and biotic stresses, and the growing demand for optimization in input use, particularly fertilizers. In this context, precision agriculture can provide the conceptual and operational basis for site-specific management, allowing fertilizer application to be adjusted to the spatiotemporal variability of the production system. This study evaluated, under commercial-scale conditions, the effects of ... G.V. Bedum, A. Colaço, M. Gelain, R. Canal Filho, J.P. Molin, E. Otavio Da Silva

363. Smart Resource Use in Precision Agriculture: A Conceptual Framework for Digital Sustainability Development

Digital and precision agriculture are expected to play a central role in shaping future sustainable food production systems by enabling more intelligent, adaptive, and resource-efficient practices. This study adopted a forward-looking literature review, composed of bibliometric and systematic aspects, to propose Smart Resource Use as an emerging paradigm for sustainable development in digitally enabled precision agriculture, extending the concept of smart consumption beyond its traditional or... B.B. Gallo, L. Visintainer Lerman, R.F. Da Silva, E. Bolfe, M.M. Da Silva, T. Alves, C.E. Pereira, T.M. Porcino

364. SMART SENSING (WEED.IT) - ZAIT Solutions for Precision Agriculture

... R. Sandri Sana, M. Nascimbem Ferraz

365. Smartphone-Based RGB Phenotyping of Hydroponic Lettuce Growth Dynamics

Hydroponic lettuce production requires frequent, accurate growth assessment to optimize yield, nutrient efficiency, and crop uniformity within short, nutrient‑sensitive cycles. Existing imaging systems can deliver such precision but are often costly or technically demanding, limiting adoption in smaller hydroponic operations. Smartphone‑based imaging offers a practical alternative for scalable proximal phenotyping, yet its cross‑device quantitative accuracy and performance under operati... L. Katz, N. Snir, K. Genkin, N. Rotbart, E. Nevo, N. Ronen, O. Reichmann

366. Software Protocol Converters as Enablers for Interoperability in Heterogeneous Multi-Robot Systems

Advancements in agricultural robotics are driving a transition toward ecosystems composed of heterogeneous platforms from multiple manufacturers. In this context, robotic platforms from multiple vendors must coexist and collaborate to perform complex tasks, including autonomous monitoring and precision weeding. However, this evolution introduces a fundamental challenge: the lack of a standardized communication stack capable of supporting cross-platform integration. The widespread use of propr... N.K. Wagner, C. Teixeira, V. Fontena, C.S. Junior, O.A. Cruz, P.H. Morgan Pereira

367. Soil Factors Affecting Severity of Verticillium Wilt in Kiwifruit (Actinidia Deliciosa)

Verticillium wilt, caused by the soil-borne fungal pathogens Verticillium albo-atrum and V. dahliae, represents a highly destructive vascular disease in kiwifruit, with pronounced impact on gold kiwi (Actinidia chinensis). The disease manifests as acute wilting, shoot dieback, foliar necrosis, and, in severe cases, complete plant mortality. Conversely, green kiwi (Actinidia deliciosa) exhibits greater resistance to Verticillium wilt; while the disease does not typically cause vine death, it a... H. Poblete, R.A. Ortega, M.M. Martinez, I. Ortega

368. Soil Structural Gradients as Drivers of Multiyear Spectral Variability: A Robust Framework for Management Zone Delineation in Heterogeneous Sugarcane Systems

Persistent soil structural gradients are widely recognized as key drivers of spatial heterogeneity in perennial cropping systems. In the heterogeneous sugarcane production environments of northwestern Argentina (Salta and Jujuy provinces), contrasting sandy and clay-rich sectors generate long-term differences in crop growth potential and resource-use efficiency. Distinguishing stable edaphic influences from transient seasonal variability is essential for reliable precision management. This st... H.J. Fernandez

369. Soil Texture Classification by Image: Deep Feature Learning vs. Handcrafted Methods for Precision Agriculture

Accurate soil texture classification is fundamental for precision agriculture, as it enables site-specific crop management that optimizes the utilization of agricultural resources and enhances overall crop productivity. This study presents a comparative analysis between features automatically extracted by a pre-trained SqueezeNet convolutional neural network (CNN) and three classical methods for manual feature extraction: Fast Fourier Transform (FFT), Gabor Filters, and Local Binary Patterns ... J.R. Favan , G.D. Faulin, F.M. Kasita Kashima, J. . Alegre, L.S. Gonçalves

370. Soil Water Nowcasting for Site-specific Yield Potential Estimation

Knowing how much plant available water (PAW) is stored across a field at key decision points in the growing season is fundamental to precision agriculture. Spatial variability in soil water translates directly into variability in water-limited yield potential, yet most growers lack the tools to quantify this at the within-field scale. Here we present a Soil Water-Energy Balance (SWEB) model that offers a framework to deliver daily, 30 m resolution estimates of PAW across any dryland padd... T. Bishop, Y. Yu, M.J. Tilse, P. Filippi

371. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine Learning

Global agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira

372. Soilres: an On-farm, Variability-centred Project to Translate Biodiversity-based Soil Health Innovations into Precision Management

SOILRES (Multi-Faceted Biodiversity Integration for a Healthy Soil and Resilient Crop Management) is a Horizon Europe project. It aims to improve soil health by a combination of agroecological practices and soil improvers which will be tested in on-farm experiments and monitored with proximal and remote sensors, as well as modelling. On-farm experiments are implemented through six use cases spanning Europe’s major pedo-climatic zones: Atlantic (Denmark), Boreal (Finland), Continental (F... M. Canicatti, D. Cammarano

373. SOLOS E PLANTAS - Updates in the Analytics Market - Brazil vs China

... R. Alves Filho

374. Soybean Yield Response to Apparent Soil Electrical Conductivity–based Management Zones

Management zone delineation is a key strategy in precision agriculture, as it enables a better understanding and managing spatial variability in soil attributes and crop yield. Apparent soil electrical conductivity (ECa) has been widely used as an indirect indicator of soil physical and chemical properties and as a basis for defining homogeneous management zones. The objective of this study was to evaluate soybean yield behavior within management zones delineated using soil ECa.The study was ... E.L. Bottega, Z.B. Oliveira, D. Queiroz, A. Luiz De Freitas Coelho, G.B. Bernhard

375. SPARC-AI: Synthetic Procedural Agricultural Rendering and Annotation Framework for Crop Phenotyping and AI Applications

Between 20% and 40% of global agricultural production is lost annually to pests and diseases, generating economic damages estimated at over US$220 billion each year. This persistent challenge underscores the urgent need for scalable, precise, and cost-effective monitoring solutions. In this scenario, Artificial Intelligence (AI) based pathogen detection systems emerge as transformative tools, enabling high-resolution spatial and temporal monitoring of crop health. However, the perfo... R. Freitas, V.S. Mello, G.D. Dallegrave, E. Farinati Leite, J.F. Valiati

376. Spatial Analysis of Physical and Sensory Attributes of Coffee Beans

Arabica coffee (Coffea arabica L.) is one of the crops with the greatest economic and social relevance in Brazil, with beverage quality being a differential of broad commercial value. This study aimed to evaluate the spatial variability of the physical and sensory attributes of coffee beans. The study was conducted during the 2023-24 crop season in a 27-hectare plot belonging to Fazenda Mandaguari, in Indianópolis, Minas Gerais, cultivated with the Topázio cultivar unde... V.M. Nunes, S.M. Hurtado, I. Almeida, A. , W.G. Siquieroli, G.P. Cândido, L.V. Lazzarini

377. Spatial and Temporal Variability of Soil Health Scores and their Relationship to Crop Yield

Soil health is the continued capacity of the soil to function as a vital living ecosystem that sustains plants, animals, and humans. To assess and evaluate soil health, the Comprehensive Assessment of Soil Health (CASH) was established. In CASH, different soil health indicators are used to assign a score. These indicators are soil properties that can vary spatially and temporally. In this study, soil health scores were determined across a landscape ... E. Estrada, S. Phillips, R. Grant

378. Spatial Data Interpolation in the AgDataBox Platform Using Graphics Processing Unit Parallelism

Precision agriculture platforms increasingly operate as integrated ecosystems that collect, store, and process large volumes of heterogeneous spatial data originating from multiple sources, including soil sampling, onboard sensors embedded in agricultural machinery, yield monitors, and remote sensing technologies such as satellites and unmanned aerial vehicles (UAVs). These platforms play a fundamental role in transforming raw georeferenced data into actionable information that supports site-... R. Sobjak, V.H. Malacarne, C.L. Bazzi, E. Souza, K. Schenatto, M. Rodrigues

379. Spatial Delineation of Site-Specific Management Units Using Vegetation Indices in Precision Agriculture

Precision Agriculture has incorporated Remote Sensing as an essential tool for characterizing the spatial variability of agricultural crops. Among the available spectral indices, vegetation indices stand out for their ability to represent vegetative vigor and spatial patterns associated with crop performance. This study aimed to evaluate the spatial stability of spectral indices obtained from a median composite for management zone delineation and to analyze their agreement with a yield map in... L.G. Kern, L. Silveira Pavão, . Müllich, I. Maldaner, L. , J. Sgarbossa, G. Kaefer Seganfredo, E. Rolim Farias Da Silva, M. Silveira Farias

380. Spatial Distribution of Coffee Leaf Miner Infestation and Its Impact on Coffee Fruit Maturation, Yield, and Beverage Quality

Differences in the maturation rate of coffee fruits can be associated with plant stress. The incidence of pests, such as the coffee leaf miner (Leucoptera coffeella), compromises the photosynthetically active area, which can reduce yield and beverage quality. Computer vision can assist in damage reduction by identifying the pest's spatial and temporal behavior. This study aimed to verify, spatially and temporally, the impact of damage caused by the coffee leaf miner on ... L.V. Lazzarini, A. , G.P. Cândido, V.M. Nunes, S.M. Hurtado, F.H. Leandro, I.D. Gonçalves

381. Spatial Distribution of the Visual Evaluation of Soil Structure in a Coffee-growing Area

Coffee production has a significant socioeconomic impact in Brazil, requiring efficient and technological management practices to ensure high yields and soil conservation. In this context, assessing soil physical quality is indispensable, and the Visual Evaluation of Soil Structure (VESS) methodology is a rapid, cost-effective, and easily applicable field alternative compared to traditional laboratory analyses. The objective of this study was to visually evaluate the structure of a soil culti... E. De Souza Salles, C. Souza, E. Pereira De Morais, O. Filho

382. Spatial Prediction of Soil Classes and Nutrients Using Random Forest in the Context of Precision Viticulture

Precision viticulture is based on modeling the spatial variability of soil, plant, and topographic attributes to support optimized management decisions. In this context, machine learning based spatial prediction algorithms have been increasingly applied for spatial interpolation. Their application in vineyards has shown strong potential to improve the representation of spatial variability and to support site-specific management strategies in viticulture. The objective of this study was to eva... F. Lasch, B. Trevizan Paese, J.M. Moura-bueno, G. Brunetto , A.A. Kokkonen, F. De Araújo Pedron, R.S. Dalmolin, L. De Paula Amaral

383. Spatial Variability in Mechanized Coffee Harvesting at Two Travel Speeds

Within the scope of Precision Agriculture (PA), monitoring the spatial variability of mechanized operations is a strategy for improving operational efficiency in coffee plantations. The evaluation of mechanized harvesting should consider, in addition to the harvested volume, the proportion of coffee effectively detached in relation to fallen coffee (FC) and remaining coffee (RC), variables directly linked to operational efficiency. Harvesting was carried out at Mariano Farm, in Poços d... N.A. Zimermam, R.P. Pereira Da Silva, L.E. Zonfrilli, A. Andrade Da Silva, T.M. De Oliveira

384. Spatial Variability of Biochemical Soil Properties and Its Relationship to Physical and Chemical Properties and Vegetation Indices

Biochemical soil properties, particularly enzymatic activity, are increasingly recognised as important indicators of soil quality within the context of regenerative agriculture. However, limited information exists regarding the spatial variability of these properties in fruit orchards at varying scales, as well as their relationships with other soil characteristics, such as texture and soil organic matter, and with plant quality. This study aimed to investigate the spatial variability... R.A. Ortega, M.M. Martinez, H. Poblete, I. Ortega

385. Spatial Variability of Foliar Nutrient Contents in a Vineyard of the Campanha Gaúcha Region

Leaf analysis is an essential tool for understanding nutrient availability, absorption, and redistribution processes in plants, providing technical support for decision-making in precision viticulture systems. The spatial variability of nutrient contents in leaf tissue is associated with soil heterogeneity, topographic conditions, and vineyard management practices. The objective of this study was to evaluate the spatial variability of macronutrients in grapevine leaf tissue, identifying distr... R. Balsamo Brondani, B.T. Paese, J.M. Moura-bueno, A.A. Kokkonen, G. Brunetto

386. Spatio-Temporal Sampling-Point Allocation for High-Density Robotic Pest Monitoring and Precision Treatment

Efficient monitoring of pests in crops, such as the two-spotted spider mite (Tetranychus urticae), is essential for optimizing pesticide application and minimizing yield losses. However, conventional manual scouting is labor-intensive and costly, limiting spatial coverage and sampling frequency. Consequently, infestation hotspots are often detected too late, reducing the effectiveness of timely and targeted interventions. This ... D. Levanon, Y. Cohen, R. Gafni, L. Shmuel, Y. Edan

387. Spatio-temporal Yield Stability in Rice-soy Rotations at Farm Scale

Integrated crop-livestock systems are facing the pressure to intensify worldwide, thus decoupling crops from pasture and reducing the amount of time under pasture, while increasing the frequency of annual grain crops. In Uruguay, rice production is commonly integrated into crop–livestock systems, generating productive and environmental advantages compared to many rice-growing regions worldwide. Recent intensification of these systems, particularly through the incorporation of soybean in... I. Macedo, &. Roel, J.J. Bonomo

388. Spatiotemporal Variability of Apple Tree Vegetative Vigor Using Proximal Sensing

The largest apple production in Brazil is located in the southern region of the country, which has a subtropical climate, borderline conditions for the production of a fruit native to temperate climates. Thus, excessive vegetative growth frequently occurs, negatively impacting productivity and quality in the orchard. This leads to the diversion of productive resources to ancillary activities, such as the application of growth regulators and green pruning, negatively affecting the producer. Cu... L. Gebler, A. De Rossi, J.T. De Abreu, E.A. Speranza, A. Sessi, L.D. Marchioretto

389. Spectral Behavior of Coffee Fruit Ripeness Using a Hyperspectral Camera

Selective harvesting is essential to ensure high beverage quality in coffee production; however, the coexistence of fruits at multiple ripeness stages on the same plant makes manual selection subjective, labor‑intensive, and time‑consuming. This preliminary study aimed to develop a non‑destructive method based on spectral information for the classification of Coffea arabica L. cv. Arara fruits at green (unripe) and yellow (ripe) stages, using images acquired on a laboratory bench with h... A. Palma Diniz Baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A.

390. Spreading Performance of a UAV for Cover Crop (Cereal Rye) Seeding at Varying Application Rates and Flight Speeds

With the increased use of UAVs for pesticide applications in agriculture, there is growing interest in their use for applying dry solid materials, especially for seeding cover crops. However, limited information currently exists on the application performance of UAVs for broadcasting cover crop seed and the effects of different operational parameters. Therefore, studies were conducted to assess the spreading performance of a DJI Agras T25 UAV under varying application rates (22.4, 33.6, 44.8,... S. Virk, J. Sizemore

391. Stability-driven Framework for Robust Plant Spectral Signature Identification

Accurate identification of agricultural crops based on spectral signatures remains a critical challenge for large-scale phytosanitary monitoring. This study proposes a stability-based structure for the robust identification of plant spectral signatures, applied to the discrimination of soybean (Glycine max) from maize (Zea mays) and cotton (Gossypium hirsutum) under biotic stress caused by the pest Spodoptera frugiperda and stink bugs. The proposed method follows a flow of proposed steps that... J. Ferreira , A.O. Françani, E. . Ferreira, L.A. Jorge, J.C. Felipe, L. Zhao

392. Standardisation Challenges in Precision Agriculture: Mapping the Landscape and Advancing Semantic Interoperability

Background: Precision agriculture increasingly depends on digital technologies and the exchange of data between equipment, sensors, platforms and decision support tools. A wide range of standards is available, including machine data formats such as ISOXML and semantic resources such as AGROVOC and rmAgro. Despite this variety, the overall standardisation landscape remains fragmented. Even within single countries, differences in code lists, vocabularies and data publishin... J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Bresilla, B. Veldhuisen

393. STARA - Evolution of Spot Spraying

... L. Seibel Sander

394. Statistical Mean Comparisons in Unreplicated Yield Trials with Georeferenced Data

Precision agriculture technologies have enabled the collection of large volumes of georeferenced yield data within experimental fields. In practice, many on-farm experiments (OFE) are implemented as large unreplicated strips or field zones containing numerous observations within each zone. The lack of replication prevents the use of classical statistical models for comparing zone means. Although many yield observations are available per zone, spatial autocorrelation violates independence assu... M. Córdoba, P. Paccioretti, M. Balzarini

395. Step-by-Step Precision Fruit Farming

Guidelines on how to implement a precision fruit farming system in perennial crops, considering abiotic aspects (fertility, water, and climate) and biotic aspects (physiology, human activities, and harvesting), in order to structure the full cycle of precision agriculture. Target audience: Technicians, Students, and Researchers Language: English   ... L. Gebler, L.H. Bassoi

396. Study of Temporal Microclimatic Variability and Its Impact on Soybean (Glycine max (L.) Merrill) Development in a Protected Environment: A Precision Agriculture Approach

Soybean, belonging to the Fabaceae family, is a leguminous crop of high economic and nutritional value, widely cultivated in Brazil, which ranks among the world’s largest producers and exporters. However, the growing demand for productivity has driven the adoption of digital technologies and Precision Agriculture (PA) methods aimed at intelligent crop management. In protected environments, cultivation allows the mitigation of external constraints, although it does not eliminate internal... J. Risardi, P. Herrmann Junior, A. Torre Neto, P. Cruvinel

397. Study on the Phenological Zoning Method for Winter Wheat in the Huang-Huai-Hai Region of China

The impact of global climate change on agricultural phenology is becoming increasingly significant. As a major producer of winter wheat, China's cultivation areas span multiple climate zones. Against the backdrop of climate change, the spatiotemporal differentiation of crop phenology has raised new scientific demands for agricultural zoning. Phenological zoning has guiding significance for variety selection, irrigation management, and pest prediction. However, existing research often reli... S. Xiaoyu, Q. Wu, Y. Ma, J. Zhang, P. Dong, X. Xu

398. Sugarcane row gaps enable the identification of critical rows for targeted interventions

Distinct patterns of sugarcane row gaps and associated plant population reduction drive the spatiotemporal variability of yield, creating a bottleneck for row prioritization in management decisions. This study tested the hypothesis that specific sugarcane rows within a field concentrate most of the linear gap lengths (LLG) and their associated economic and productive losses, consistent with the Pareto Principle. The objective was to identify rows that are critical in terms of LLG occurrence, ... E. Otavio Da Silva, J.P. Molin, R. Canal Filho, M.R. Cherubin

399. Sun-view Geometry Causes Hotspot Effect in UAV Imagery During Summer in Tropical Regions

High-resolution imagery acquired by Unmanned Aerial Vehicles (UAVs) is essential for remote sensing applications. However, the high solar elevation around noon during summer in tropical regions produces a hotspot effect when the camera is mapping at nadir. The sun-view alignment generates a bright spot in each image acquired during the flight, which significantly changes the digital number and, consequently, the estimated surface reflectance. The objective of this research was to analyze the ... W. Maes, L. Rodrigues , A.M. Tommaselli, R.P. Silva, V. Carreira

400. SYNC SOIL - From Pixel to Decision: The Journey of Soil Data in Precision Agriculture

... E. Fernandes

401. System-based Precision Agriculture for Sustainable Crop Production

The major challenge addressed is the systemic mismanagement of nitrogen (N) fertilizer in agricultural fields leading to problems such as leaching of nitrates into groundwater and emission of harmful greenhouse gases. Digital technologies are commercialized in agriculture (available from the early 1990s) but have failed with N fertilization. Despite agriculture is the least digitized sector (as highlighted at the last World Economic Forum) to make a reliable recommendation, researchers need t... D. Cammarano, S. Ata-ul-karim, M. Canicatti, D. Abalos, Y. Zhou, T.S. Tanaka, K. Butterbach-bahl

402. Systematic Multi-criteria Assessment of Soil Analysis Technologies for an Agricultural Living Lab: Readiness Levels and Field Applicability for Citrus and Sugarcane Production

Precision Agriculture (PA) fundamentally depends on accurate and timely edaphic diagnostics for site-specific decision-making. Within the scope of the Smart B100 Advanced Research Center (CCD-SB100/IAC), funded by FAPESP, an agricultural Living Lab is being structured. While the project's initial focus includes citrus and sugarcane in São Paulo State, the foundational soil sensing technologies evaluated are crop-agnostic. Currently, traditional laboratory methods for soil analysis ... J. Sanches , H. Fischer, M.C. De Almeida, C.K. Luvizzoto, C.E. Otoboni

403. Temporal Dynamics of Chlorophyll Indices in Corn Leaves in Response to Top-dressing with Organo-mineral Fertilizer

Nitrogen is one of the main limiting factors for maize productivity, and managing it efficiently is fundamental for sustainable agriculture. Understanding how different nitrogen fertilization strategies affect plant physiological parameters throughout the crop cycle is essential for optimizing nutrient use efficiency. This study aimed to evaluate how chlorophyll indices (total, a, and b) in maize leaves vary throughout the cycle in response to different rates of organo-mineral fertilizer appl... L.C. E. Ribeiro, M. Antônio, A. Batista Santos, E. Sousa Meneses, C.C. Santana

404. Temporal Extrapolation of N-uptake Maps Retrieved from Satellite Imagery Aiming to Overcome Availability Issues Due to Cloudy Days

For a series of satellite-based functionalities, including in-field crop nitrogen variable rate fertilization, an up-to-date satellite image is required. Due to cloudy conditions, actual images, depending on weather, may be highly compromised or unavailable. To overcome this issue, and aiming to obtain an image showing the actual field status, a simple crop growth model to extrapolate on time the crop growth of the last available image was created and implemented in a fully automatized proces... G. Portz, S. Reusch

405. Temporal NDRE Dynamics from UAS Imagery to Characterize Rice Drought Response

Characterizing drought resilience in rice remains challenging under increasing climate variability. Drought tolerance is a complex and dynamic trait that is difficult to quantify using traditional field phenotyping approaches, particularly when responses vary with time. High-throughput temporal phenotyping with unmanned aircraft systems (UAS) enables monitoring of canopy reflectance dynamics associated with water stress across the growing season. This study evaluated whether ...

406. Temporal Stability of Management Zones Derived from Vegetation Indices and Yield Data in Contrasting Production Systems

The delineation of management zones is a central component of site-specific crop management in precision agriculture. However, the temporal stability of zones derived from different data sources remains a key challenge, particularly when vegetation indices and yield data are combined across multiple seasons. This study evaluates the temporal stability of management zones delineated using vegetation indices and yield data derived from long-term commercial field datasets. The proposed meth...

407. Temporal Variability of Vegetation Indices and Spatial Autocorrelation Applied to Specific Management in Mountain Coffee Crops

The characterization of spatial and temporal variability in agricultural crops is an important stage for the application of precision agriculture, enabling the definition of management zones and the prescription of inputs for variable rate application. In this context, the use of unmanned aerial vehicles (UAVs) equipped with multispectral sensors has enabled the acquisition of spectral data with spatial and temporal resolutions compatible with the objectives of the intended activity. Asso... I. Araújo Barbosa, D. Queiroz, A.L. Coelho, D. Sárvio Valente, M.C. Moreira, B. Costalonga Vargas

408. Temporal Variations in Leaf Nutrient Concentrations and Photosynthetic Nutrient Use Efficiency in Durian

This study assessed leaf nutrient concentration in durian leaves and evaluated the photosynthetic use efficiency of nitrogen (N), phosphorus (P), and potassium (K) on durian trees over two fruiting seasons. This study also provided nutrient recommendations for durian fertilization using the Diagnosis and Recommendation Integrated System (DRIS) and optimal nutrient management range. A total of 63 mature trees were selected for leaf sampling. All samples were collected at two growth stages in e... M.F. Omar, S.K. Balasundram, N.E. Tajidin

409. Textural Indices from Multispectral Images Improve Leaf Nitrogen Prediction in Corn Using Machine Learning Models?

The pursuit of more accurate models for predicting nitrogen (N) in corn has driven the exploration of variables beyond traditional spectral indices. While effective, vegetation indices often fail to capture the full complexity of the canopy structure, and conventional methods like leaf analysis are constrained by their point-in-time nature. Recent studies have demonstrated that Gray Level Co-occurrence Matrix (GLCM) texture features derived from low-altitude remote sensing platforms can signi... A. Batista Santos, E. Sousa Meneses, L.C. E. Ribeiro, M. Antônio, C.C. Santana

410. The Agdatabox Platform Supports Teaching and Research in Precision Agriculture.

The AgDataBox platform is an initiative being developed by Brazilian institutions to support small producers and service providers seeking to work with Precision and Digital Agriculture. Among its objectives, it aims to integrate data, software, and methodologies for those wishing to work with precision and digital agriculture through APIs that provide not only data storage support but also the availability of various services, such as automated functionalities for generating thematic maps an... C.L. Bazzi, R. Sobjak, K. Schenatto, E.G. Souza, E. Cely Bonilla

411. The Agronomic and Bioeconomic Aspects of Site-Specific Seeding Rates and Depths for Winter Wheat in Lithuania

Precision seeding is one of the most important agrotechnological solutions for smart agriculture. It exploits the variability of soil properties in the field to increase the agronomic and economic efficiency of crops. This study investigated the impact of site-specific seeding (SSS) on the yield and productivity parameters of winter wheat in Lithuania, as well as its economic benefits, compared with conventional uniform rate seeding (URS). Experiments were conducted in a field divided into fi... Z. Kriauciuniene, M. Kazlauskas, K. Romaneckas, S. Buragiene, I. Bručienė, E. Šarauskis

412. The Future of Precision & Digital Agriculture for a More Sustainable and Profitable Agriculture

... E. Lund

413. The Influence of Field Geometry on the Operational Stability of Uav-based Spraying

The use of spraying drones has expanded rapidly in precision agriculture; however, operational factors such as field geometry may compromise application stability. This study aimed to evaluate the influence of field shape on operational variability and operational capacity during spraying performed with a DJI Agras T100 drone. The experiment was conducted in two fields with distinct geometries: a regular (rectangular) field and an irregularly shaped field, located at the Technology Developmen... A. Felipe Dos Santos, R.Q. Alvez, T.O. Barboza, M.C. Arnosti, G.F. Valdez , G.L. Silveira

414. To Mist or Not to Mist: Using Site-Specific Sensed Data to Evaluate Infrastructure Needs in a Vineyard

This study explores the use of high-resolution site-specific data to understand how sensors can impact infrastructure purchases in a real-world precision agriculture growing operation. Since 2023, over 60 sensors have been deployed at Laurel Grove Wine Farm, a vineyard in Winchester, Virginia, including information on weather (temperature, humidity, sunlight, and rainfall) and soil (moisture content and pH). The Sensor Collection and Remote Environment Care Reasoning Operation (SCARECRO) syst... M. Everett, K. Wing, D. Onyeoguzoro, D. Mommen, J. Shovic

415. TOPCON - The Future of Precision Agriculture is No Longer Precision: It is Operational Simplicity

... B. Lucio

416. Topographic Modeling Using Remotely Piloted Aircraft to Identify Areas with Water Erosion Potential and to Plan Sowing Lines

Water erosion constitutes one of the main factors of agricultural soil degradation. In this context, knowledge of the topography of agricultural fields and the planning of sowing lines guided by geotechnologies emerges as a strategy to mitigate surface runoff and soil loss. This study aimed to perform the topographic modeling of an agricultural field and to analyze the effect of using different sowing line designs on the longitudinal slope of these lines. The study was conducted in an agricul... L. Silveira Pavão, A. Müllich, E. Rolim Farias Da Silva, R. Cavalcanti, M. Silveira De Farias, I. , J. Sgarbossa, L.

417. Towards Precision Agriculture with Electrochemical Sensors for Detecting Dopamine in Plants and Fruits

Cathecolamines are essential neurotransmitters that regulate the central nervous system of animals, while also acting as direct modulators in plants, coordinating antioxidant responses and ionic balance regulation. Dopamine (DA) is an essential catecholamine not only for animals but also plays a critical regulatory role in plants, acting as a potent antioxidant and growth modulator under abiotic stresses such as drought and salinity or pathogen attacks and helps neutralize free radicals ... L.M. Gonçalves, B. Vasconcellos Lopes, B.B. Gallo, A. La Rosa, D.A. Fruchtenicht, C. Miler, P. Silveira, N.L. Carreno

418. Towards Trusted Satellite Data for Precision Farming: Mitigating Spoofing and Improving Data Integrity Using Galileo OSNMA and HAS and Copernicus Traceability Service

Background: Precision agriculture increasingly relies on GNSS positioning not only to execute field operations with high spatial accuracy, but also to provide trustworthy data for documentation, certification, and regulatory compliance. However, GNSS signals remain vulnerable to degradation, jamming, and especially spoofing—an intentional manipulation of satellite signals causing machinery to believe it is in a different position. Such incidents have already been observ... B. Veldhuisen, T. Bresilla, J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Van Der Wal

419. Tracking Multi-Nutrient Dynamics in Spring Crops Using Field Hyperspectral Imaging and Chemometrics

Within crop nutrient ecology we still lack a clear and growth-stage-specific understanding of which nutrient elements are most limiting in field conditions and during different periods of the growing season, and how the (co-)limitation pattern is influenced by different management conditions. This is particularly relevant in Nordic systems with short growing seasons, where nutrient constraints can appear rapidly. At the same time, precision nutrient management still requires robust non-destru... E. Lennartsson, J. Oliveira, M. Weih

420. Transforming Agronomic Tables into Continuous Sufficiency and Fertilizer-rate Functions for Digital Recommendation Systems

Soil-test interpretation tables and fertilizer recommendation tables are widely used in agronomic practice, but they typically classify results into discrete categories (e.g., very low, low, medium, and high). While this format is suitable for manual consultation, it introduces artificial “jumps” between classes and limits automation when implementing diagnostic and recommendation rules in computerized systems. In this study, we developed a two-step methodology to convert these ta... D. Fernandes Paiva, G.M. Chaer

421. UAV-Based Detection and Precision Management of Cirsium arvense: An End-to-End Workflow from Deep Learning to Variable-Rate Spraying

Unmanned aerial vehicles (UAVs) combined with deep learning can enable site-specific weed management by transforming high-resolution imagery into actionable prescription maps for precision spraying. This study presents and validates an end-to-end operational workflow for detecting Cirsium arvense under real field conditions and converting detections into sprayer-compatible management zones. A 24.61 ha arable field in northwestern Hungary was surveyed using a multirotor UAV equipped w... M. László

422. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery Applications

In animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion

423. Understanding Adoption and Post-Adoption Impacts of Smart Farming Technologies in Italy

Smart farming technologies (SFTs) are increasingly promoted as key enablers of agricultural efficiency, resource optimization, and environmental sustainability. However, despite rapid technological advancement, empirical evidence on realized economic and resource-use impacts under real farming conditions remains limited, creating uncertainty about the magnitude and distribution of impacts. Existing evidence remains largely focused on perceived drivers, barriers, and intentions to adopt, rathe...

424. Unified Detection and Weight Estimation of Small Fruits Using Multi-Task Vision Models in Precision Agriculture

This work presents a single computer vision model that can perform both object detection and image-level regression from the same input image. Many real applications, especially in agriculture, need information about individual objects as well as a global measurement for the entire image. When analyzing an image of small fruits such as different types of berries, grapes, currants, and muscadine grapes, it may be necessary to detect and classi... P. Sundaravadivel, T. Stroud, S. Borah, B.J. Sampson, P. Knight, S.P. Kumpatla, J.F. Ross

425. Universal Dataset Constructor & Preprocessing Framework for Earth Observation AI Tasks in Digital Agriculture

The rapid advancement of Artificial Intelligence (AI) in digital agriculture is increasingly dependent on the ability to fuse heterogeneous data sources. While Earth Observation (EO) data from Sentinel and Landsat missions provides a backbone for monitoring, high-performance models for yield prediction and land management require a more holistic approach. This paper presents a Universal Dataset Constructor & Preprocessing Framework designed to automate the generation of combined, multimod... V. Sorokina, I. Klinkov, D. Yablonski, S. Henkler, A. Zakhary

426. Unlocking Partially Annotated Agricultural Data: A Cut-and-Paste Data Augmentation Framework for Plant Detection

Accurate weed and crop recognition is essential for effective management practices in precision agriculture, enabling targeted herbicide application through automated spraying systems. However, the performance of deep learning models in real-world field settings is often limited by class imbalance, where broad categories such as monocotyledons and dicotyledons overshadow classes labelled at the species level. A major bottleneck in addressing this imbalance is the massive under-utilization of ... M. Madsen, , R.N. Jørgensen

427. Unveiling Research Patterns in Precision Agriculture: A Comprehensive Network Analysis of ICPA Proceedings

The International Conference on Precision Agriculture (ICPA) is one of the most influential global forums dedicated to advancing technologies, methodologies, and scientific understanding in the domain of precision agriculture. Since its inception, the conference has served as a central platform for disseminating innovations in data-driven crop management, sensor technologies, spatial analysis, automation, and decision-support systems. Now in its 17th edition, the ICPA has accumulated more tha... S. Camargo, J. Valiati

428. Upscaling UAV Image-Trained Machine Learning Models from Research Plots to Commercially Cropped Land

High-throughput plant phenotyping (HTPP) leverages the advancement of unmanned aerial vehicles (UAVs) technology, paired with improvement in spectral sensing technology to allow for the derivation of plant phenotypic traits from image analysis. Crop breeding programs continue to increase incorporation of HTTP methods into their pipelines to enhance their efficiency of selecting for varieties. Machine learning (ML) models, often used hand in hand with HTTP methods, generate phenotypic trait pr... W. Maess, S. Shirtliffe, K. Nketia

429. Use of a Chlorophyll Meter As a Decision-support Tool for Nitrogen Management in Coffee Fields

Coffee is a crop with high nitrogen (N) demand in Brazil; therefore, improving N use efficiency in coffee plantations can have significant environmental and economic impacts. Decisions regarding N application rates in coffee cultivation are often empirical or based on multiple factors, including expected yield and plant N status as determined by leaf N concentration. Traditionally, leaf N concentration is obtained through laboratory chemical analysis, a process that may take several days and ... D. Almeida, M. Pereira, A. , V. Giroto, G. Portz

430. Use of Digital Permeameter for the Functional Characterization of Geoenvironments

Characterizing agricultural geoenvironments with precision is inherently a complex task. Historically, this process has relied on quasi-static edaphic attributes, such as soil texture and apparent electrical conductivity. However, a critical problem exists, as these parameters exhibit low sensitivity to ephemeral structural changes resulting from soil management systems. Texture conditions the productive potential, yet it fails to reflect modifications in pore geometry induced by mechanical p... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes

431. Use of Multispectral UAV Imagery to Monitor Late-Season Defoliation in Peanut Production Systems

Reduced leaf area late in the growing season is commonly associated with lower physiological activity in peanut plants, particularly when foliar diseases intensify near harvest. As canopy biomass declines, peg strength may be compromised, increasing the risk of pod loss during digging operations if harvest is delayed. Although canopy biomass decreases become more noticeable near harvest, visual field assessments do not always reflect belowground conditions, making it difficult to determine th... R. Dias Borges, C. Pilcon, A. Felipe Dos Santos, L. Lacerda, C. Rossi

432. Use of Profitability Maps in Precision Agriculture As a Strategic Tool for Decision-making

Brazilian agribusiness plays a significant role in the national economy, accounting for approximately 27% of the Gross Domestic Product, while operating in an environment characterized by substantial exposure to climatic, operational, and market risks. In commodity-based production systems, where farmers act as price takers, economic efficiency fundamentally depends on effective cost and revenue management, as well as the proper allocation of productive resources. In this context, this study ... W. Nart Macedo, L. Gebler

433. Use of Textural and Spectral Data in Predictive Modeling of Sugarcane Yield

Sugarcane is one of the most important crops in Brazil, playing a strategic role in the production of sugar, ethanol, and bioenergy. Efficient monitoring of crop yield is essential for agricultural management and decision-making; however, conventional yield estimation methods are generally labor-intensive, destructive, and inefficient in capturing spatial variability within fields. In this context, the use of remote sensing techniques integrated with machine learning models emerges as a promi... L. Rodrigues , S. Luns, G. Rolim, T. Canata, V. Carreira

434. Using Hyperspectral Imagery to Monitor Peanut Physiological Responses to Water Stress

Peanut production in Georgia plays an important role in the United States agriculture, as it is the country’s largest peanut producer. However, increasing climate variability poses major risks in peanut productivity, particularly through drought and heat stress. This study aimed to detect and monitor physiological responses of nine peanut genotypes under irrigated and drought conditions using high-resolution hyperspectral imaging (HSI). A field trial was conducted in the 2025 season at ...

435. Using VIS-NIR spectroscopy to predict Water-Extractable Soil Phosphorus content in Texas Vertisols

  Phosphorus (P) is an essential nutrient for plant growth. However, excessive P application can result in P accumulation in agricultural soils, increasing the risk of P losses to water sources. Water-extractable P (Pw) data are essential for assessing the risk of environmental P losses. Investigating field-scale variability of Pw using visible–near infrared spectroscopy (VIS–NIR) remains limited. This study aimed to develop an empirical relationship between Pw, soil ...

436. Utilization of Proximal Remote Sensing As a Non-destructive Method for Assessing the Quality of Corn Seeds

The physiological quality of corn seeds plays a key role in crop establishment. It directly influences final productivity. Although germination and vigor tests are well established, they have practical limitations. These tests are time-consuming. They require laboratory infrastructure and can involve destructive procedures. These factors limit their use in situations demanding faster, scalable assessments. In this scenario, proximal remote sensing has gained attention as a practical, non-dest... A.E. Dos Reis Rodrigues, M.A. Da Silva, J.D. Rodrigues Oliveira, G. Ribeiro Silva, A. Lopes De Brito Filho, M.G. Da Silva Brochado, N.G. Krohn, F. Morlin Carneiro

437. Validation of UAV-Based NDVI Using Proximal Sensing in Mountain Coffee Plantations

Unmanned aerial vehicles (UAVs) with multispectral sensors have expanded remote sensing applications in precision agriculture, however validation against ground-truth measurements remains critical, especially for perennial crops in complex terrain such as coffee plantations in mountainous regions. Proximal sensing can be employed as a reference for evaluating the quality of data obtained by remote platforms. This study aimed to evaluate the reliability of Normalized Difference Vegetation Inde... B. Costalonga Vargas, I.A. Barbosa, D. Queiroz, A.L. Coelho, D.M. Valente, M.C. Moreira

438. Variable Seeding Rate to Manage Within-field Variability

Within-field variability can strongly influence final crop yield and the efficiency of agricultural inputs such as seeds, fertilizer, water, and agrochemicals, thus managing spatial variability through precision agriculture to optimize input use and improve sustainability can yield significant gains, provided that the mechanisms driving field variability are understood. Despite extensive research on the relationship between seeding density and yield, relatively little attention has been given... B. Maestrini, L.P. Pott, D. Bamberg, T. Liska, T. Rosado, L. Sander, T. Ruiz Moreno, N. Garcia Dutrez, F. Doeler, T. Van Der Wal, V. Kaster Marini, T. Amado, Nieuwenhuizen

439. Vegetation Indices Behavior in Soybean Management Zones Based on Apparent Soil Electrical Conductivity

Understanding the spatial variability of crop development is essential for improving decision-making in precision agriculture. Vegetation indices derived from remote sensing have been widely used to monitor crop growth, biomass accumulation, and spatial heterogeneity within agricultural fields. When combined with management zones, these indices can provide valuable insights into the relationship between soil properties and crop performance. The objective of this study was to evaluate the beha... E.L. Bottega, Z.B. Oliveira, F.D. Mundstock, M.C. Sausen, G.B. Bernhard

440. Verification of the Practices of the European Union Regulatory Framework and Comparative Analysis with the Capabilities and Requirements of Good Practices in Precision Seeding in Brazil.

Global precision and digital agriculture is undergoing a period of technological and regulatory transition. Faced with the growing global demand for operational efficiency, Brazilian technology for active seed metering and guidance at high speeds emerges as a high value-added solution. However, the commercialization of these components in the European market requires technical harmonization between Brazilian practices and the bloc's certification requirements. This work aims to verify the... J. Santos, V. Kaster Marini

441. Web Application Based on CNN for Classification of Biotic and Abiotic Stresses in Coffee Leaves

The use of digital systems can assist coffee growers and professionals in diagnosing stresses that affect coffee plantations, ensuring that crop management is carried out correctly and efficiently. Therefore, the aim of this study was to develop a web application based on a pre-trained Convolutional Neural Network to classify coffee leaf images exhibiting symptoms of biotic and abiotic stresses. Initially, a dataset consisting of coffee leaf images affected by biotic and abiotic stresses was ... D.H. Leite, D.S. Valente, P.M. Arruda, F.D. Tancredi, D. Queiroz, G. Dumbá Monteiro De Castro

442. Weed identification in soybean fields using RGB UAV imagery acquired at different flight altitudes

The presence of weeds in agricultural fields is one of the main factors reducing crop productivity due to competition for light, water, and nutrients. In this context, digital agriculture and the use of unmanned aerial vehicles (UAVs) enable the acquisition of high-resolution imagery for detecting and monitoring these weeds. However, increasing flight altitude reduces spatial resolution, compromising the identification of key visual attributes (shape, texture, and edges) and making it more di...

443. Weed mapping: advantages of RGB CNN-based approaches vs multispectral pixel-based methods

Weed detection remains a major challenge in modern agriculture, and accurate weed mapping is crucial to support rapid and efficient management interventions, ensuring crop productivity and economic viability. In this context, geotechnologies such as remote sensing and computer vision, together with the widespread adoption of drones, enable the acquisition of ultra–high spatial resolution imagery, allowing more detailed analyses in complex agricultural environments. Although multispectra...

444. What 255 Sugarcane Farms and 15,000 Ha Reveal About Sustainable Nutrient Management

Considering spatial and temporal variability in agricultural production is a key pathway toward improving sustainability in broadacre systems. Cropping systems under uniform management (UM) assumptions inherently neglect this variability, creating substantial inefficiencies and environmental risks. In Brazil, sugarcane occupies approximately 9 million hectares and underpins a bioenergy sector often cited as contributing to one of the most renewable energy matrices worldwide. However, fertiliz... R. Canal Filho, J.P. Molin, L. De Goes Sterle, V. Ferraz

445. Who is the Agricultural Practitioner of the Future?

An agricultural industry that continues to adopt new technologies and rely on data-driven decisions demands a unique skillset from its practitioners that goes beyond traditional agricultural training. Despite this demand, relatively few technology-focused agriculture programs are available at the post-secondary level worldwide. In 2020, Olds College of Agriculture & Technology (Alberta, Canada) created a 2-year diploma in Precision Agriculture (launched in 2020) and a 4-year Bachelor of D... D. Karran, B. Hoffos, F.H. Karp

446. Worldwide Crop Precision Agriculture Adoption: 2026 Update

Robotics, machine vision, and drone spraying have attracted much attention in recent years. But the technologies introduced in the 2000s and earlier, such as yield monitors, guidance, variable rate technology (VRT), and digital imagery continue to advance worldwide, more on large mechanized grain and oilseed farms. This update summarizes census estimates and adoption surveys with statistically reliable random sampling from 24 countries. Within-country and farm size breakdowns are reported whe... B. Erickson, J. Mcfadden, E. Morimoto, J. Lowenberg-deboer

447. Yield Stability in Continuous Corn Under Three Years of Nitrogen Management: Linking UAV Derived Vegetation Indices to Temporal Variability

Continuous corn production systems are highly sensitive to nitrogen (N) management, and year to year variability in weather conditions can strongly influence crop performance and yield stability. Understanding how different N fertilization strategies affect yield evaluation and fertilizer recommendations from remote sensing platforms, consistency across multiple growing seasons, is essential for the development of future decision support systems. Because vegetation indices (VIs) derived from ... E. Lord, E. Fallon, A. Cambouris

448. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean pod

Accurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aim...