Proceedings :: International Society of Precision Agriculture

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Gil da Silva, B.J

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Authors

Karasinski, M.A

Macedo, E

Costa, R

Peixoto, A.S

Duarte, D.S

Gil da Silva, B.J

Ferreira, E.J

Jorge, L.A

Bendahan, A.B

de Medeiros, R.D

Galvão, M.P

Barreto, G.F

Duarte, D.S

Dantas Oliveira, S.V

Melville, C

Gabriel da Silva Carmo, I.L

Karasinski, M.A

Gil da Silva, B.J

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Poster

Year

2026

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Filter results2 paper(s) found.
1. Weed mapping: advantages of RGB CNN-based approaches vs multispectral pixel-based methods Weed detection remains a major challenge in modern agriculture, and accurate weed mapping is crucial to support rapid and efficient management interventions, ensuring crop productivity and economic viability. In this context, geotechnologies such as remote sensing and computer vision, together with the widespread adoption of drones, enable the acquisition of ultra–high spatial resolution imagery, allowing more detailed analyses in complex agricultural environments. Although multispectral...
2. Weed identification in soybean fields using RGB UAV imagery acquired at different flight altitudes The presence of weeds in agricultural fields is one of the main factors reducing crop productivity due to competition for light, water, and nutrients. In this context, digital agriculture and the use of unmanned aerial vehicles (UAVs) enable the acquisition of high-resolution imagery for detecting and monitoring these weeds. However, increasing flight altitude reduces spatial resolution, compromising the identification of key visual attributes (shape, texture, and edges) and making it more difficult...

Filter results2 paper(s) found.

1. Weed mapping: advantages of RGB CNN-based approaches vs multispectral pixel-based methods

Weed detection remains a major challenge in modern agriculture, and accurate weed mapping is crucial to support rapid and efficient management interventions, ensuring crop productivity and economic viability. In this context, geotechnologies such as remote sensing and computer vision, together with the widespread adoption of drones, enable the acquisition of ultra–high spatial resolution imagery, allowing more detailed analyses in complex agricultural environments. Although multispectral...

2. Weed identification in soybean fields using RGB UAV imagery acquired at different flight altitudes

The presence of weeds in agricultural fields is one of the main factors reducing crop productivity due to competition for light, water, and nutrients. In this context, digital agriculture and the use of unmanned aerial vehicles (UAVs) enable the acquisition of high-resolution imagery for detecting and monitoring these weeds. However, increasing flight altitude reduces spatial resolution, compromising the identification of key visual attributes (shape, texture, and edges) and making it more difficult...