Efficient data-driven management of grapevine water status is central to optimizing both yield and wine quality under changing climatic conditions. This study demonstrates how continuous sensing and modeling of stem water potential (SWP) can be used as an indicator for adaptive irrigation and stress management in vineyards.
Over six consecutive seasons (2014–2019), high-resolution datasets from a Cabernet Sauvignon vineyard were integrated, combining meteorological data, irrigation logs, SWP measurements, and yield and wine-quality metrics. Using advanced analytics, including random forest modeling, ANOVA, and fuzzy c-means clustering, we characterized SWP seasonal curves (SWP-SCs) as dynamic signatures of vine–environment interactions.
Machine learning identified temperature as the dominant factor shaping SWP patterns, followed by relative humidity and precipitation. Clustering analysis revealed three distinct vine–water status regimes:
- Moderate stress (Cluster 1) – balanced performance and optimal yield.
- Moderate-to-severe stress (Cluster 2) – similar yields but superior wine quality, with an optimal SWP threshold of −1.5 to −1.6 MPa before harvest.
- Extreme stress (Cluster 3) – substantial decline in both yield and wine quality.
By fusing physiological data with environmental monitoring, the study demonstrates the potential of digital phenotyping and decision-support systems for precision irrigation. These insights pave the way for AI-based vineyard management platforms that can predict stress conditions and recommend irrigation adjustments in real-time.
In an era of rising temperatures and variable rainfall, dynamic, sensor-driven irrigation control emerges as a cornerstone of smart viticulture, ensuring both sustainability and premium wine quality.
