A practical data-driven approach for precise stem water potential monitoring in pistachio and almond orchards using supervised machine learning algorithms

The advent of machine learning technologies in conjunction with the advancements in UAV-based remote sensing pioneered a new era of research in agriculture. The escalating concern for water management in drought-prone areas such as California underscores the urgent need for sustainable solutions. Stem water potential (SWP) measurement using pressure chambers is one of the most common methods used to directly determine tree water status and the optimal timing for irrigation in orchards. However, this approach is inefficient due to its labor-intensive nature. To address this problem, we used weather, thermal and multispectral data as inputs to the machine learning (ML) algorithms to predict the SWP of pistachio and almond trees. For each crop, we first deployed six supervised ML classification models: Random Forest (RF), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). All classifiers provided more than 79% of accuracy while RF showed high performance in both pistachio and almond orchards at 88% and 89%, respectively. The feature importance results by the RF model revealed that the weather features were the most influential factors in the decision- making process. In both crops, canopy temperature T was the next important feature closely followed by OSAVI in pistachios and NDVI in almonds. RF regression model predicted SWPs with R2 of 0.70 in pistachio and R2 of 0.55 in the almond orchard. Our results demonstrate that ML models are practical tools for irrigation scheduling decisions. This study offered a data-driven approach that effectively balances minimal data requirements with accuracy to facilitate optimal water management for end-users.