Proceedings :: International Society of Precision Agriculture

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Valdivino, R

Vacari, I

Villegas, D

Veldhuisen, B

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Authors

Speranza, E.A

Inamasu, R.Y

Romani, L.A

Naime, J

Sobjak, R

Vacari, I

Bazzi, C.L

Shibusawa, S

Gomez-Candon, D

Cabeza, A

Villegas, D

Casas, A.M

Igartua, E

Teixeira, S.A

Valdivino, R

Tsukahara, R

Ribeiro, M

Topics

Decision Support Systems, Cloud Platforms, and Open Data Solutions

UAV-Based Scouting, Imaging, and Targeted Applications

Precision Crop Protection, Pest, and Plant Health

Type

Oral

Year

2026

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Filter results3 paper(s) found.
1. 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. This... E.A. Speranza, R.Y. Inamasu, L.A. Romani, J. Naime, R. Sobjak, I. Vacari, C.L. Bazzi, S. Shibusawa Download paper
2. 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 rainfed... D. Gomez-candon, A. Cabeza, D. Villegas, A.M. Casas, E. Igartua Download paper
3. 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 throughput... S.A. Teixeira, R. Valdivino, R. Tsukahara, M. Ribeiro Download paper

Filter results3 paper(s) found.

1. 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. This... E.A. Speranza, R.Y. Inamasu, L.A. Romani, J. Naime, R. Sobjak, I. Vacari, C.L. Bazzi, S. Shibusawa

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2. 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 rainfed... D. Gomez-candon, A. Cabeza, D. Villegas, A.M. Casas, E. Igartua

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3. 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 throughput... S.A. Teixeira, R. Valdivino, R. Tsukahara, M. Ribeiro

Download paper