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Junior, D.U
Benevenuti, F
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Authors
Lima, C.D
Nogueira, B
, A
Junior, D.U
Carvalho, I.R
Bredemeier, C
Marañon Aguilar, E
Kastensmidt, F
Benevenuti, F
Gonzalez Aguilera, C
Topics
Remote and Proximal Sensing of Soils and Crops
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Poster
Year
2026
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1. Analysis of Mixed Models in UAV-based Spectral Vegetation Indices for Prediction of Agronomic Variables in Soybean Subjected to Flooding

The identification of soybean genotypes with increased flooding tolerance is relevant for yield stability in lowlands producing areas. In this context, the use of relevant spectral vegetation indices based on multispectral sensors embedded in unmanned aerial vehicles (UAVs) for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics. Nonetheless, the environmental effects can change the accuracy of spectral indices and the correct methodology for deduction... C.D. Lima, B. Nogueira, A. , D.U. Junior, I.R. Carvalho, C. Bredemeier

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