Proceedings :: International Society of Precision Agriculture

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Garcia Ramirez, D.Y

González Zarate, O.J

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Jimenez Lopez, F.R

Jimenez, A

Ruge Ruge, I.A

Garcia Ramirez, D.Y

Jimenez Lopez, F.R

Jimenez, A

Garcia Ramirez, D.Y

González Zarate, O.J

Macea Zabaleta, L

Castillo Ojeda, N

Flórez Olivera, A.F

Rodolfo, T.A

Gonzalez Aguilera, C

Topics

Agricultural Robotics, Automation, and Mechanization

Precision Crop Protection, Pest, and Plant Health

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Oral

Year

2026

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Authors

Filter results3 paper(s) found.
1. 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 disturbances... F.R. Jimenez Lopez, A. Jimenez, I.A. Ruge Ruge, D.Y. Garcia Ramirez Download paper
2. 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-time... F.R. Jimenez Lopez, A. Jimenez, D.Y. Garcia Ramirez Download paper
3. 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

Filter results3 paper(s) found.

1. 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 disturbances... F.R. Jimenez Lopez, A. Jimenez, I.A. Ruge Ruge, D.Y. Garcia Ramirez

Download paper

2. 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-time... F.R. Jimenez Lopez, A. Jimenez, D.Y. Garcia Ramirez

Download paper

3. 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