Proceedings
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| Filter results7 paper(s) found. |
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1. Predictive Analysis of Fertilizer Efficiency with Machine LearningFertilizers play a key role in agribusiness, both as an essential input for agricultural productivity and as a strategic component in the commercial chain. They provide nutrients that are indispensable for soil correction and crop growth, such as nitrogen, phosphorus, and potassium, allowing the soil to maintain its capacity to sustain crops even after several harvests. It is estimated that about 50% of global food production depends on the use of fertilizers, and in Brazil, these inputs represent... C.S. Santos, R.K. Weber |
2. Automated Initial Plant Stand Assessment in Bean Crops Using Uav-based Yolov8 DetectionThe use of RGB images acquired by unmanned aerial vehicles (UAVs), combined with artificial intelligence techniques, has increased significantly in recent years for object identification and crop monitoring in agriculture. These technologies enable rapid plant stand count, facilitating decision-making processes. However, limited information is available regarding the optimal flight height for identifying bean plants at early growth stages. Therefore, the objective of this study was to evaluate... G. Valdes Fernandez , G. Lacerda Da Silveira, R. Fernandes Queiroz Alves , T. Costa Barboza, M.C. Arnosti, A. Felipe Dos Santos, W.B. Da Silva, O. Pereira Da Costa |
3. Economic and Ecological Performance and Farm-level Adoption of Market-available Tools for Variable-rate Nitrogen Management in a Region of Small-to-medium-scale AgricultureThe proposed contribution combines the results of extensive multi-year field trials on variable rate-nitrogen fertilization (VRN) of winter wheat with the results of a series of farmer surveys, and findings drawn from a government investment subsidy program. All three data sources (field trials, surveys, investment subsidy program) cover roughly the same period and agricultural area. The field trials were conducted from 2023 to 2025, the surveys in 2020, 2022, and 2025, and data on the investment... M. Gandorfer, B. Vinzent, J. Pfrombeck, J. Garnitz, F. Maidl |
4. Comparative Analysis of YOLOv3–YOLOv12 Architectures for Automatic Oil Palm Detection in Agricultural MonitoringOil palm (Elaeis guineensis) is considered the most productive oilseed crop worldwide, and Brazil holds one of the greatest global potentials for palm oil production. Efficient monitoring of cultivated areas is therefore essential for proper crop management, enabling the detection of planting gaps, yield estimation, and decision-making support. In this context, computer vision techniques based on deep learning models, particularly those from the YOLO (You Only Look Once) family, have... M.C. Arnosti, A. Felipe Dos Santos, T. Costa Barboza, L.S. Souza Pinto, E. Amaral, G. Lacerda Da Silveira, G. Valdes Fernandez |
5. Multi-Band UAV-Borne SAR Sensitivity (C, L, and P Bands) for Detecting Leaf-Cutting Ant Nests in Eucalyptus PlantationsPlanted forests in Brazil cover approximately 10.5 million hectares and are recognized worldwide for sustainable management and the supply of bioproducts derived from renewable raw materials. In addition, the country stands out in pulp production and exports, ranking second only to the United States. However, the planted forest sector has faced phytosanitary challenges, particularly related to leaf-cutting ants, which cause biomass losses and reduce leaf area, compromising photosynthetic capacity... W. Batista Da Silva , A. Santos, T. Costa Barboza, O.P. Costa, G. Valdes Fernandez , M. Ciscato, G. . Silveira, R. . Filho |
6. Evaluation of the Performance of Computer Vision Models in the Detection and Counting of Tomato Plants Infected by Tomato Spotted Wilt Virus (TSWV)Tomato is one of the most economically important vegetable crops worldwide. However, this crop is severely affected by Tomato Spotted Wilt Virus (TSWV), whose transmission occurs mainly through thrips. Thus, identifying infected plants is an important step to reduce the dissemination and infection of healthy plants, reducing economic losses. Computer vision-based models have been widely used in the automated detection of plant diseases. In this context, this work aimed to evaluate the performance... L.S. Souza Pinto, S. . Azevedo, M. . Medeiros, A. Felipe Dos Santos, T. Costa Barboza, M.C. Arnosti, G. Valdes Fernandez , G. Lacerda Da Silveira |
7. Assessment of Spatiotemporal Variability in Desiccation Efficiency Using a Spray Drone Through Vegetation IndicesThe increasing adoption of spray drones in precision agriculture has raised important questions regarding operational parameters and their influence on herbicide performance under field conditions. Although unmanned aerial spraying systems offer advantages such as reduced soil compaction, greater operational flexibility, and rapid field coverage, the interaction between flight parameters and droplet deposition dynamics remains insufficiently understood. This study aimed to evaluate the spatial... G. Lacerda Da Silveira, M.C. Arnosti, G. Valdes Fernandez , T. Costa Barboza, A. Felipe Dos Santos, E. Amaral |