Proceedings
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| Filter results18 paper(s) found. |
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1. Unveiling Research Patterns in Precision Agriculture: A Comprehensive Network Analysis of ICPA ProceedingsThe International Conference on Precision Agriculture (ICPA) is one of the most influential global forums dedicated to advancing technologies, methodologies, and scientific understanding in the domain of precision agriculture. Since its inception, the conference has served as a central platform for disseminating innovations in data-driven crop management, sensor technologies, spatial analysis, automation, and decision-support systems. Now in its 17th edition, the ICPA has accumulated more than... S. Camargo, J. Valiati |
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. Variable Seeding Rate to Manage Within-field VariabilityWithin-field variability can strongly influence final crop yield and the efficiency of agricultural inputs such as seeds, fertilizer, water, and agrochemicals, thus managing spatial variability through precision agriculture to optimize input use and improve sustainability can yield significant gains, provided that the mechanisms driving field variability are understood. Despite extensive research on the relationship between seeding density and yield, relatively little attention has been given... B. Maestrini, L.P. Pott, D. Bamberg, T. Liska, T. Rosado, L. Sander, T. Ruiz Moreno, N. Garcia Dutrez, F. Doeler, T. Van Der Wal, V. Kaster Marini, T. Amado, Nieuwenhuizen |
4. 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 |
5. Validation of UAV-Based NDVI Using Proximal Sensing in Mountain Coffee PlantationsUnmanned 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 |
6. Importance of Irradiance Correction for UAV-based Vegetation Indices in the Prediction of Shoot Biomass in WheatThe extraction of vegetation indices from multispectral images obtained with UAV-bsed sensors for biomass estimation has proven to be a useful tool for designing site-specific interventions on wheat. The reflectance values from monochromatic bands collected with optical sensors for calculating vegetation indices should have high reliability and repeatability in the case of successive assessments under different illumination conditions. In this context, the objective of the study was to characterize... A.C. Figueiró, B. Nogueira, E. Bender, R. Silva, V.M. Cassol, S.J. Silveira , C. Bredemeier, A.L. Vian |
7. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in SoybeanThe conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed control... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian |
8. 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 |
9. Mobile Edge AI for Detection of Grape Clusters and Disease Symptoms in VineyardsPrecision viticulture demands accessible technological solutions that enable rapid disease diagnosis and production monitoring directly in the field. In real-world production contexts, dependence on cloud connectivity, external servers, or specialized hardware limits the adoption of computer vision tools by small and medium-sized farmers. In this context, this work presents a solution based on artificial intelligence embedded in a mobile application for the detection of grape bunches and leaves... E.M. Da Silveira, F.I. Nogueira, S.D. Camargo, A. Freire Campos, J. Valiati, E.F. Leite |
10. Estimation of Agronomic Parameters in Maize Using UAV-based Vegetation Indices Obtained by a Multispectral SensorPrecision agriculture emerges as a response to optimize input use and to monitor crop spatial variability over time. In this context, the use of vegetation indices obtained by optical sensors embedded in drones or satellites has become a useful tool for predicting agronomic parameters in maize fields, such as aboveground dry biomass, leaf chlorophyll content, and grain yield. Thus, the objective of this study was to correlate field agronomic parameters with vegetation indexes obtained by a multispectral... B. Nogueira, A.C. Figueiró, A. , E. Bender, C.D. Lima, A.L. Vian, C. Bredemeier |
11. From Render to Field: Detecting Asian Soybean Rust Using Models Trained Exclusively on Synthetic ImageryTraining machine learning models for crop disease detection requires large, annotated datasets that are costly and difficult to obtain under variable field conditions. Asian Soybean Rust (ASR) can reduce soybean yield by up to 90% and costs Brazilian producers over US$2 billion per season in fungicide applications and yield losses. Despite this impact, existing machine learning studies on ASR remain scarce with no publicly available RGB... L.B. Fontoura, A. De Freitas, E. Farinati Leite, J. Valiati |
12. 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 |
13. Integration of Spectrotemporal Metrics and Machine Learning for Soybean Grain Yield PredictionEstimating agricultural grain yield in heterogeneous production environments remains one of the main challenges of digital agriculture. Although vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge (NDRE), are widely used to describe canopy vigor, their predictive capacity strongly depends on how temporal information is represented and on the structure of the model employed to integrate this spectral variability. The present study... L. Rossetto Gerlach, A.L. Vian, C. Bredemeier, T. Enderle, T. Santos Cocco, M. Wrubleski |
14. 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 |
15. Estimation of carbon sequestration in agricultural crops using the C-Questro softwareContemporary agriculture faces the challenge of reconciling productivity with climate impact mitigation, positioning soil and plant biomass carbon sequestration as a strategic pillar for global sustainability. However, carbon quantification at the field scale still encounters hurdles due to high-cost methodologies or operational complexity. The objective of this work was to develop and validate a Python-based software, named "C-Questro," designed to automate the estimation of carbon... |
16. 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 |
17. Interaction Between Biological Nitrogen Fixation and Urea Rates on Nitrogen Accumulation and Yield in MaizeNitrogen (N) is a fundamental nutrient for maize, with a direct impact on the crop’s yield potential, and it represents one of the highest costs in the production system. The possibility of biologically fixing N emerges as a potential strategy for managing crops in a more economical and efficient manner, reducing dependence on mineral nitrogen fertilizers and promoting greater physiological balance in the crop. A wide range of biological products has been introduced with the aim of competing... T. Enderle, T. Santos Cocco, M. Wrubleski, L. Rossetto Gerlach, A.L. Vian |
18. Agro Extensão: Extensão Rural DigitalUniversity extension plays a fundamental role in bridging the gap between academia and society, especially in the agricultural sector, where technical information can determine the success of an operation. A historical example of this relevance is Operation Tatu (1960), which was essential in transforming the agricultural model in Rio Grande do Sul, promoting the shift from rudimentary practices to a technological approach. This initiative became a landmark demonstration of the potential of university... F. Baudini, A.L. Vian, M. Wrubleski, T. , G. Lima Leal |