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Barreto, B.B
Barbosa, I.A
Baltazar, J.D
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Authors
Costalonga Vargas, B
Barbosa, I.A
Queiroz, D
Coelho, A.L
Valente, D.M
Moreira, M.C
Baltazar, J.D
Coelho, A.L
de Arruda Viana, L
Brandão, A.D
Valente, D.S
Furtado Jr, M.R
Ferraz, V
Mariotto Nabarro, L
Castanho Fernandes, R
Barreto, B.B
Ricardo Silva Costa, B
Molin, J.P
Botta de Siqueira, D.A
Molin, J.P
Barreto, B.B
Silva, M.L
Fiorio, P.R
Costa, B
Barreto, B.B
Fantin Gebler, H
Molin, J.P
Topics
Remote and Proximal Sensing of Soils and Crops
Agricultural Robotics, Automation, and Mechanization
Predictive Modeling, Phenotyping, and Digital Tools for Decision Support
Site-Specific Nutrient, Lime and Seed Management
Type
Poster
Oral
Year
2026
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1. Validation of UAV-Based NDVI Using Proximal Sensing in Mountain Coffee Plantations

Unmanned aerial vehicles (UAVs) with multispectral sensors have expanded remote sensing applications in precision agriculture, however validation against ground-truth measurements remains critical, especially for perennial crops in complex terrain such as coffee plantations in mountainous regions. Proximal sensing can be employed as a reference for evaluating the quality of data obtained by remote platforms. This study aimed to evaluate the reliability of Normalized Difference Vegetation Index... B. Costalonga Vargas, I.A. Barbosa, D. Queiroz, A.L. Coelho, D.M. Valente, M.C. Moreira

2. Performance of Autonomous Navigation in an Agricultural Tractor Using Pure Pursuit Control and Dubins Path Planning

Autopilot systems in agricultural machinery are primarily designed to ensure accurate tracking of predefined routes. However, their performance is strongly influenced by the internal parameters of the control algorithms employed, which may vary according to operational conditions. In this context, computational simulations constitute a valuable tool for investigating these interactions and identifying optimal operating configurations. This study evaluates an autopilot system based on the Pure... J.D. Baltazar, A.L. Coelho, L. De Arruda Viana, A.D. Brandão, D.S. Valente, M.R. Furtado Jr

3. Generating Data Via Operational Monitoring of Backpack Equipment on Small Farms

The Brazilian coffee industry, a global leader in production and exports, faces the challenge of increasing production efficiency to meet growing worldwide demand while preserving natural resources. Precision Agriculture (PA) offers essential tools for this data-driven sustainable intensification; however, its adoption in regions with rugged topography and by family-based growers is severely limited by the scarcity of accessible technologies, particularly for spatial yield measurement. The semi-mechanized... V. Ferraz, L. Mariotto Nabarro, R. Castanho Fernandes, B.B. Barreto, B. Ricardo Silva Costa, J.P. Molin

4. Definition of Flight Height for Image Monitoring of Pupunha Palm Cultivation

The use of Unmanned Aerial Vehicles (UAVs) associated with computer vision has expanded the applications of precision agriculture, especially in perennial crops that require detailed spatial monitoring. In the peach palm tree, the automated detection of clumps from aerial images is an efficient alternative to traditional methods, contributing to management and production estimates. Thus, the objective of this study was to evaluate the influence of flight height on the performance of YOLO... D.A. Botta De Siqueira, J.P. Molin, B.B. Barreto, M.L. Silva, P.R. Fiorio

5. Assessing Soil Fertility Inequality at Regional Scale to Support Plot-level Site-specific Management

In accordance with Precision Agriculture (PA) principles, site-specific management could be performed in small-scale farming systems, assuming a cell-size approach where between-plot variability is manageable, rather than the within-plot variability. This framework is particularly useful for fertilization strategies within a single farm and may be extended to a macro scale when georeferenced datasets from multiple plots and farms across a region of interest are available. However, when dealing... B. Costa, B.B. Barreto, H. Fantin Gebler, J.P. Molin