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Kastensmidt, F
Suarez, F
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Authors
Suarez, F
Gómez Montenegro, B
Dottori, C
Alemandri, V
de Breuil, S
Bruno, C
García Seleme, F
Marañon Aguilar, E
Kastensmidt, F
Benevenuti, F
Gonzalez Aguilera, C
Topics
Predictive Modeling, Phenotyping, and Digital Tools for Decision Support
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Poster
Year
2026
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1. A Methodological Framework for Modeling Plant Virus Occurrence Using Biometeorological Data: Insights from Multi-crop Case Studies in Argentina

Viral diseases represent a major threat to the productive stability of agricultural systems. Their spatial and temporal occurrence is influenced by environmental conditions that regulate interactions among viruses, vectors, and hosts, making disease anticipation difficult using statistical traditional approaches. This situation highlights the need to understand the dynamics of the different biological components capable of affecting agricultural systems, and design and apply tools that facilitate... F. Suarez, B. Gómez Montenegro, C. Dottori, V. Alemandri, S. De Breuil, C. Bruno, F. García Seleme

2. Hardware–Software Co-Design of Quantized CNN Inference for Edge AI in Precision Agriculture

Precision agriculture increasingly relies on real-time automated inspection systems to ensure crop quality and reduce manual labor in grain handling processes. Manual visual inspection, traditionally used for grain quality assessment, is inherently limited by low throughput, subjectivity, and high labor costs. To address these issues, automated vision-based inspection systems have been widely adopted in industrial environments, enabling high-throughput and consistent grain classification. Recent... E. Marañon Aguilar, F. Kastensmidt, F. Benevenuti, C. Gonzalez Aguilera