Machine Learning‐Enabled Intelligent Technologies Across the Grape and Wine Value Chain: From Vineyard Sensing to Winemaking Optimization

Machine learning is transforming the grape and wine industry by shifting traditional experience-driven practices toward data-driven and intelligent decision making across the entire value chain. This review provides a conceptually driven synthesis of machine learning-enabled technologies spanning vineyard sensing, precision viticulture, fermentation monitoring, and winemaking optimization. An integrated analytical framework is proposed, linking multisource data acquisition, preprocessing and representation learning, model development, and intelligent decision making into a unified end-to-end pipeline. Building on this framework, the review critically examines advances related to five key scientific challenges: multimodal data integration, model interpretability, cross-domain generalization, whole-chain decision coordination, and scalable industrial deployment. Particular emphasis is placed on the mechanisms and trade-offs of multiscale sensing technologies (including spectroscopy, chromatography-mass spectrometry, imaging, and electronic sensing), as well as data preprocessing, feature engineering, and multimodal fusion strategies. A task-oriented and data-structured perspective on model selection is highlighted, covering linear models, kernel methods, ensemble learning, deep neural networks, and probabilistic frameworks, alongside evaluation protocols and interpretability approaches. In contrast to fragmented task-specific studies, this review highlights the importance of cross-stage integration and closed-loop decision systems linking vineyard management with downstream vinification and quality evaluation. Despite rapid progress, key challenges remain, including data scarcity and heterogeneity, limited model transferability, and high implementation costs. Emerging directions such as knowledge-guided machine learning, causal inference, small-sample learning, and human-AI collaboration are expected to enhance robustness and interpretability. Overall, this review provides a structured roadmap for advancing intelligent and sustainable practices in the grape and wine industry.