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Thomé Barbosa, R.N

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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

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

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Precision Dairy, Livestock, and Animal Welfare Monitoring

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Poster

Year

2026

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Authors

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. 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...

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. 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...