Proceedings :: International Society of Precision Agriculture

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Chaves, C

Carvalho de Arruda, D

C. C. Daher, L

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Authors

H. S. Sousa , F

S. Maciel, T

M. dos Reis, M

Santos, R.D

M. Santos, A

M. S. de Souza, A

K. F. Veras, A

G. Ferreira, G

P. M. Nunes, M

C. R. Seruffo, M

C. C. Daher, L

G.M. Silva, A

Hermes, M

Albuquerque, M

Andreoli, A

, C

Chaves, C

Gonçalves Junior, S.R

da Costa Salem, M

Eissmann Souza, G

Carvalho de Arruda, D

Bender, E

Nogueira, B

Espindola Muller, L

Gallo, B.B

Bredemeier, C

Chaves, C

Quicaña, A.

Chimello, L

Hermes, M

Andreoli, A

Albuquerque, M

Figueiredo, G

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

Remote and Proximal Sensing of Soils and Crops

Digital Solutions for Soil Health, Water Quality, and Conservation Practices

Type

Poster

Oral

Year

2026

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1. 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 in... 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
2. 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 their... M. Hermes, M. Albuquerque, A. Andreoli, C. , C. Chaves
3. 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-destructive... 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 Download paper
4. 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 pressures... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes

Filter results4 paper(s) found.

1. 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 in... 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

2. 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 their... M. Hermes, M. Albuquerque, A. Andreoli, C. , C. Chaves

3. 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-destructive... 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

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4. 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 pressures... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes