Abstract
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 šš was the next important feature closely followed by
OSAVI in pistachios and NDVI in almonds. RF regression model predicted SWPs with š
2 of 0.70 in pistachio
and š
2 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.
https://www.sciencedirect.com/science/article/pii/S0168169925001103
Figure: Flowchart of the research investigating whether machine learning can predict stem water potential in pistachio and almond orchards.
