Proceedings :: International Society of Precision Agriculture

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Bezerra , C.R

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Authors

Karasinski, M.A

Thomé Barbosa, R.N

Melville, C

Peixoto, A.S

Ferreira, E.J

Jorge, L.A

Bendahan, A.B

Galvão, M.P

Bezerra, C.R

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

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Precision Dairy, Livestock, and Animal Welfare Monitoring

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Poster

Oral

Year

2026

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Filter results3 paper(s) found.
1. Integration of LiDAR-Derived TWI and UAV Multispectral Data for Waterlogging Susceptibility Mapping in Precision Agriculture Topography directly controls water redistribution across the landscape, shaping the spatial variability of soil moisture in agricultural areas. The Topographic Wetness Index (TWI), derived from digital elevation models, is widely used to estimate the potential for water accumulation; however, its field-scale validation supported by high-resolution multispectral drone imagery remains limited. In agricultural systems, recurrent waterlogging can reduce productivity by impairing germination, promoting...
2. 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...
3. 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 results3 paper(s) found.

1. Integration of LiDAR-Derived TWI and UAV Multispectral Data for Waterlogging Susceptibility Mapping in Precision Agriculture

Topography directly controls water redistribution across the landscape, shaping the spatial variability of soil moisture in agricultural areas. The Topographic Wetness Index (TWI), derived from digital elevation models, is widely used to estimate the potential for water accumulation; however, its field-scale validation supported by high-resolution multispectral drone imagery remains limited. In agricultural systems, recurrent waterlogging can reduce productivity by impairing germination, promoting...

2. 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...

3. 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