Proceedings :: International Society of Precision Agriculture

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Dantas Oliveira, S.V

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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

Karasinski, M.A

Costa, R

Melville, C

Macedo, E

Gabriel da Silva Carmo , I.L

Dantas Oliveira, S.V

Galvão, M.P

Bendahan, A.B

Bezerra, C.R

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Poster

Oral

Year

2026

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Filter results2 paper(s) found.
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...
2. Generation of Ultra-High-Resolution Synthetic Data via Generative Super-Resolution to Support UAV Image Annotation and Model Training Manual annotation of imagery acquired by unmanned aerial vehicles (UAVs) for detection/segmentation tasks is one of the main bottlenecks for deep learning applications in precision agriculture, due to the high cost and the time required to produce consistent labels. In addition, low-altitude flights to obtain ultra–high spatial resolution increase operational complexity and data volume, limiting the scalability of acquisition campaigns. Although neural network–based super-resolution... M.A. Karasinski, R. Costa, C. Melville, E. Macedo, I.L. Gabriel Da Silva Carmo , S.V. Dantas Oliveira, M.P. Galvão, A.B. Bendahan, C.R. Bezerra Download paper

Filter results2 paper(s) found.

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...

2. Generation of Ultra-High-Resolution Synthetic Data via Generative Super-Resolution to Support UAV Image Annotation and Model Training

Manual annotation of imagery acquired by unmanned aerial vehicles (UAVs) for detection/segmentation tasks is one of the main bottlenecks for deep learning applications in precision agriculture, due to the high cost and the time required to produce consistent labels. In addition, low-altitude flights to obtain ultra–high spatial resolution increase operational complexity and data volume, limiting the scalability of acquisition campaigns. Although neural network–based super-resolution... M.A. Karasinski, R. Costa, C. Melville, E. Macedo, I.L. Gabriel Da Silva Carmo , S.V. Dantas Oliveira, M.P. Galvão, A.B. Bendahan, C.R. Bezerra

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