Proceedings :: International Society of Precision Agriculture

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Flórez Olivera, A.F

Fortinis, H

Fischer, H

Feldman, M

Flynn, K

Fischer, H

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Authors

González Zarate, O.J

Macea Zabaleta, L

Castillo Ojeda, N

Flórez Olivera, A.F

Rodolfo, T.A

Gonzalez Aguilera, C

Nze Memiaghe, J.D

Adhikari, K

Smith, D.R

Messiga, A

Flynn, K

Mazega, M

Fortinis, H

Fischer, H

Luvizotto, C.K

Otoboni, C.E

de Almeida, M.C

Rolon, R

Fischer, H

Otoboni, C.E

Luvizotto, C.K

de Almeida, M.C

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

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

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Agricultural Robotics, Automation, and Mechanization

Type

Oral

Poster

Year

2026

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Filter results4 paper(s) found.
1. 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 manual... O.J. González Zarate, L. Macea Zabaleta, N. Castillo Ojeda, A.F. Flórez Olivera, T.A. Rodolfo, C. Gonzalez Aguilera Download paper
2. 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 chemical...
3. 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 solutions... M. Mazega, H. Fortinis, H. Fischer, C.K. Luvizotto, C.E. Otoboni, M.C. De Almeida Download paper
4. 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

Filter results4 paper(s) found.

1. 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 manual... O.J. González Zarate, L. Macea Zabaleta, N. Castillo Ojeda, A.F. Flórez Olivera, T.A. Rodolfo, C. Gonzalez Aguilera

Download paper

2. 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 chemical...

3. 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 solutions... M. Mazega, H. Fortinis, H. Fischer, C.K. Luvizotto, C.E. Otoboni, M.C. De Almeida

Download paper

4. 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