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Barreto, G.F
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Authors
Karasinski, M.A
Bendahan, A.B
Jorge, L.A
Gabriel da Silva Carmo , I.L
Barreto, G.F
Peixoto, A.S
Dantas Oliveira, S.V
Thomé Barbosa, R.N
Schurt, D.A
Topics
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Poster
Year
2026
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1. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean pod

Accurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aimed...