Proceedings
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| Filter results11 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. 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 |
3. 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 |
4. 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 |
5. 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 |
6. 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 |
7. 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 |
8. 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 |
9. 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... |
10. 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 |
11. 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 |