  Effective monitoring of walnut water status and stress level across the whole
orchard is an essential step towards precision irrigation management of
walnuts, a significant crop in California. This study presents a machine
learning approach using Random Forest (RF) models to map stem water potential
(SWP) by integrating high-resolution multispectral remote sensing imagery from
Unmanned Aerial Vehicle (UAV) flights with weather data. From 2017 to 2018,
five flights of an UAV equipped with a seven-band multispectral camera were
conducted over a commercial walnut orchard, paired with concurrent ground
measurements of sampled walnut plants. The RF regression model, utilizing
vegetation indices derived from orthomosaiced UAV imagery and weather data,
effectively estimated ground-measured SWPs, achieving an $R^2$ of 0.63 and a
mean absolute error (MAE) of 0.80 bars. The integration of weather data was
particularly crucial for consolidating data across various flight dates.
Significant variables for SWP estimation included wind speed and vegetation
indices such as NDVI, NDRE, and PSRI.A reduced RF model excluding red-edge
indices of NDRE and PSRI, demonstrated slightly reduced accuracy ($R^2$ =
0.54). Additionally, the RF classification model predicted water stress levels
in walnut trees with 85% accuracy, surpassing the 80% accuracy of the reduced
classification model. The results affirm the efficacy of UAV-based
multispectral imaging combined with machine learning, incorporating thermal
data, NDVI, red-edge indices, and weather data, in walnut water stress
estimation and assessment. This methodology offers a scalable, cost-effective
tool for data-driven precision irrigation management at an individual plant
level in walnut orchards.
