Integrating Time-Series Meteorological Data and sUAS Information into a Machine Learning Framework for California Vineyard Water Stress Monitoring

Efficient irrigation management is fundamental to sustainable crop production, particularly under increasing temperatures and limited water availability. In vineyards, water stress significantly influences grapevine development and productivity. Controlled water stress is intentionally applied in deficit-irrigated systems to regulate yield and enhance fruit quality. Therefore, vineyards must be routinely monitored to prevent excessive stress that could cause detrimental effects. In this study, we developed a machine-learning framework based on the eXtreme Gradient Boosting (XGB) machine-learning model to estimate grapevine leaf water potential (Y _leaf ) using meteorological data and high-resolution imagery from small unmanned aerial systems (sUAS) over commercial vineyards of different varieties and in different climatic zones in California. The framework incorporates key meteorological and image-derived features, including maximum air temperature in the 24 hours prior to the flight, air temperature at the time of flight, the difference between these two temperatures, as well as canopy temperature derived from sUAS thermal imagery. These features were included to indirectly capture plant-water-weather interaction during the 24-hour period preceding data collection, enhancing the model’s practical applicability. The XGB model demonstrated robust performance, achieving an RMSE of 0.16 MPa, a bias of -0.06 MPa, and a correlation coefficient of 0.83 while minimizing computational cost. Model generalizability was further validated in an independent vineyard, demonstrating its potential for commercial application in precision irrigation and vineyard water management. Our research highlights the potential for broader applicability, particularly in addressing flash drought and promoting adaptive water resource management.