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Moura-Bueno, J.M
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Authors
Lasch, F
Trevizan Paese, B
Moura-Bueno, J.M
Brunetto , G
Kokkonen, A.A
de Araújo Pedron, F
Dalmolin, R.S
de Paula Amaral, L
Balsamo Brondani, R
Paese, B.T
Moura-Bueno, J.M
Kokkonen, A.A
Brunetto, G
Topics
Predictive Modeling, Phenotyping, and Digital Tools for Decision Support
Site-Specific Nutrient, Lime and Seed Management
Type
Poster
Year
2026
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1. Spatial Prediction of Soil Classes and Nutrients Using Random Forest in the Context of Precision Viticulture

Precision 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. Spatial Variability of Foliar Nutrient Contents in a Vineyard of the Campanha Gaúcha Region

Leaf analysis is an essential tool for understanding nutrient availability, absorption, and redistribution processes in plants, providing technical support for decision-making in precision viticulture systems. The spatial variability of nutrient contents in leaf tissue is associated with soil heterogeneity, topographic conditions, and vineyard management practices. The objective of this study was to evaluate the spatial variability of macronutrients in grapevine leaf tissue, identifying distribution... R. Balsamo Brondani, B.T. Paese, J.M. Moura-bueno, A.A. Kokkonen, G. Brunetto