  Early detection and management of grapevine diseases are important in
pursuing sustainable viticulture. This paper introduces a novel framework
leveraging the TabPFN model to forecast blockwise grapevine diseases using
climate variables from multi-sensor remote sensing imagery. By integrating
advanced machine learning techniques with detailed environmental data, our
approach significantly enhances the accuracy and efficiency of disease
prediction in vineyards. The TabPFN model's experimental evaluations showcase
comparable performance to traditional gradient-boosted decision trees, such as
XGBoost, CatBoost, and LightGBM. The model's capability to process complex data
and provide per-pixel disease-affecting probabilities enables precise, targeted
interventions, contributing to more sustainable disease management practices.
Our findings underscore the transformative potential of combining Transformer
models with remote sensing data in precision agriculture, offering a scalable
solution for improving crop health and productivity while reducing
environmental impact.
