Proceedings :: International Society of Precision Agriculture

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Prudenciatto Clemente, E

Maes, W

Macedo, E

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Authors

Maes, W

Rodrigues , L

Tommaselli, A.M

Pereira da Silva, R.P

Carreira, V

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

Karasinski, M.A

Thomé Barbosa, R.N

Costa, R

Duarte, D.S

Bezerra , C.R

Costa, N.L

Bendahan, A.B

Jorge, L.A

Macedo, E

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

UAV-Based Scouting, Imaging, and Targeted Applications

Precision Dairy, Livestock, and Animal Welfare Monitoring

Type

Poster

Oral

Year

2026

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Filter results4 paper(s) found.
1. Sun-view Geometry Causes Hotspot Effect in UAV Imagery During Summer in Tropical Regions High-resolution imagery acquired by Unmanned Aerial Vehicles (UAVs) is essential for remote sensing applications. However, the high solar elevation around noon during summer in tropical regions produces a hotspot effect when the camera is mapping at nadir. The sun-view alignment generates a bright spot in each image acquired during the flight, which significantly changes the digital number and, consequently, the estimated surface reflectance. The objective of this research was to analyze the presence... W. Maes, L. Rodrigues , A.M. Tommaselli, R.P. Silva, V. Carreira
2. 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...
3. Comparative Evaluation of Ground Point Classifiers in LiDAR Point Clouds for DEM Generation in Pasture Areas The classification of ground points in LiDAR point clouds is an essential step for generating reliable Digital Terrain Models (DTMs), particularly in livestock production systems based on pastures. Despite methodological advances in forested and urban environments, studies specifically addressing ground classification in pasture areas remain limited, where the proximity between the forage canopy and the ground surface makes altimetric distinction between classes challenging. The heterogeneous...
4. 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 results4 paper(s) found.

1. Sun-view Geometry Causes Hotspot Effect in UAV Imagery During Summer in Tropical Regions

High-resolution imagery acquired by Unmanned Aerial Vehicles (UAVs) is essential for remote sensing applications. However, the high solar elevation around noon during summer in tropical regions produces a hotspot effect when the camera is mapping at nadir. The sun-view alignment generates a bright spot in each image acquired during the flight, which significantly changes the digital number and, consequently, the estimated surface reflectance. The objective of this research was to analyze the presence... W. Maes, L. Rodrigues , A.M. Tommaselli, R.P. Silva, V. Carreira

2. 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...

3. Comparative Evaluation of Ground Point Classifiers in LiDAR Point Clouds for DEM Generation in Pasture Areas

The classification of ground points in LiDAR point clouds is an essential step for generating reliable Digital Terrain Models (DTMs), particularly in livestock production systems based on pastures. Despite methodological advances in forested and urban environments, studies specifically addressing ground classification in pasture areas remain limited, where the proximity between the forage canopy and the ground surface makes altimetric distinction between classes challenging. The heterogeneous...

4. 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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