Proceedings
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| Filter results8 paper(s) found. |
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1. Spatial Prediction of Soil Classes and Nutrients Using Random Forest in the Context of Precision ViticulturePrecision viticulture is based on modeling the spatial variability of soil, plant, and topographic attributes to support optimized management decisions. In this context, machine learning based spatial prediction algorithms have been increasingly applied for spatial interpolation. Their application in vineyards has shown strong potential to improve the representation of spatial variability and to support site-specific management strategies in viticulture. The objective of this study was to evaluate... F. Lasch, B. Trevizan Paese, J.M. Moura-bueno, G. Brunetto , A.A. Kokkonen, F. De Araújo Pedron, R.S. Dalmolin, L. De Paula Amaral |
2. Estimating Grape Bunch Yield Using Convolutional Neural Networks and Proximal RGB Imaging in the Brazilian PampaViticulture of fine wines has become an increasingly important economic activity in the Pampa biome of southern Brazil, a relatively recent production frontier with approximately two decades of commercial development. In this emerging region, accurate prediction of grapewine productivity represents one of the most relevant challenges for growers, as reliable early estimates directly support decision-making related to harvest planning, logistics, labor allocation, and market... S. Camargo, E.M. Da Silveira, F.I. Nogueira, A.F. Campos, V.Z. Mércio |
3. Influence of Terrain Attributes on the Spatial Variability of Soil Macronutrients in Vineyards of Southern BrazilNutrient variability in vineyards directly affects grapevine development and grape yield, highlighting the importance of appropriate nutritional management, since optimizing soil nutrient levels contributes to improved grape, and, consequently, wine quality. The objective of this study was to evaluate the spatial variability of soil macronutrient distribution in a vineyard and to correlate it with terrain attributes. The study was conducted in a 10-ha commercial Pinot Noir vineyard located in... A. Costa Tolfo, B. Trevizan Paese, J.M. Moura Bueno, A.A. Kokkonen, G. Brunetto, S. Schemmer |
4. Relationship Between Soil Classes and Grape Yield in a Vineyard of the Campanha Gaúcha RegionBrazilian viticulture has shown significant expansion in recent decades. However, this productive growth has brought new challenges for vineyard management, particularly regarding the understanding of soil spatial variability and its relationship with grape yield and quality. The objective of this study was to correlate the spatial variability of soil classes with grape yield. The study was conducted in a commercial vineyard located in Santana do Livramento, in the Campanha Gaúcha region,... B. Baumgardt, B. Trevizan Paese, J.M. Moura Bueno, G. Brunetto, A.A. Kokkonen, A. Benetti |
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. Transforming Agronomic Tables into Continuous Sufficiency and Fertilizer-rate Functions for Digital Recommendation SystemsSoil-test interpretation tables and fertilizer recommendation tables are widely used in agronomic practice, but they typically classify results into discrete categories (e.g., very low, low, medium, and high). While this format is suitable for manual consultation, it introduces artificial “jumps” between classes and limits automation when implementing diagnostic and recommendation rules in computerized systems. In this study, we developed a two-step methodology to convert these tables... D. Fernandes Paiva, G.M. Chaer |
7. Monitoring the Invasive Grass Eragrostis plana with Artificial Intelligence: A Comparative Study of Hyperspectral Data and Drone-Based Object DetectionThe invasion of exotic plant species is recognized as one of the major threats to biodiversity and ecosystem stability worldwide. In the Brazilian Pampa biome, Eragrostis plana Nees (commonly known as Annoni grass) has become one of the most aggressive invasive species since its introduction in the 1950s. Currently occupying approximately 20% of the native grassland vegetation in the state of Rio Grande do Sul, this species exhibits high adaptive capacity, rapid propagation, and the absence of... S. Camargo, N. Perez, T.S. Lopes, A.R. Silveira |
8. SYNC SOIL - From Pixel to Decision: The Journey of Soil Data in Precision Agriculture... E. Fernandes |