Proceedings :: International Society of Precision Agriculture

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Duarte, D.S

De Ross Marchioretto, L

Diniz Dalmolin, R.S

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Authors

Moura Bueno, J.

Rech, L.F

Diniz Dalmolin, R.S

de Paula Amaral, L

Buana, I

De Araujo Pedron, F

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

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

UAV-Based Scouting, Imaging, and Targeted Applications

Precision Dairy, Livestock, and Animal Welfare Monitoring

Type

Poster

Year

2026

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Authors

Filter results3 paper(s) found.
1. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron
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...

Filter results3 paper(s) found.

1. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates

The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron

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...