Proceedings :: International Society of Precision Agriculture

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Jaconis , S.Y

Jin, C

Jimenez Lopez, F.R

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Authors

Jimenez Lopez, F.R

Jimenez, A

Ruge Ruge, I.A

Garcia Ramirez, D.Y

Jimenez Lopez, F.R

Jimenez, A

Garcia Ramirez, D.Y

Li, Y

Jin, C

Zhang, X

Dhaliwal, A

Bastos, L

SV, K

Bhattarai, A

Jakhar, A

Poudel, K

McCallister, D.M

Jaconis , S.Y

Topics

Agricultural Robotics, Automation, and Mechanization

Precision Crop Protection, Pest, and Plant Health

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Oral

Poster

Year

2026

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Filter results4 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. 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 shape... Y. Li, C. Jin, X. Zhang Download paper
4. 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 differences... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis

Filter results4 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. 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 shape... Y. Li, C. Jin, X. Zhang

Download paper

4. 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 differences... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis