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Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 1   
Received: 05 Jan., 2026    Accepted: 07 Feb., 2026    Published: 19 Feb., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Vineyard microclimate plays a critical role in determining grape quality and ultimately influences wine characteristics. This study systematically investigates the effects of key microclimatic factors, including temperature, light, humidity, and wind speed, on grape physicochemical properties and flavor compounds. A comprehensive evaluation system for grape quality was established, integrating both traditional indicators and secondary metabolites. Multi-source microclimate data were collected through sensor networks and processed using advanced data fusion techniques. Various modeling approaches, including statistical models, machine learning algorithms, and mechanistic models, were applied to quantify the relationships between microclimate variables and grape quality. The models were validated and optimized to improve predictive accuracy and robustness. A case study conducted in a representative vineyard demonstrated the practical applicability of the proposed framework and provided insights into optimized vineyard management strategies. The results highlight the importance of microclimate regulation and offer a scientific basis for precision viticulture and quality improvement.

Vineyard microclimate; Grape quality; Modeling; Machine learning; Precision viticulture