Predicting Tart Cherry Stem Water Potential Using UAV Multispectral Imagery and Environmental Data via Symbolic Regression

Authors

Anderson L. S. Safre, University of CaliforniaFollow
Alfonso Torres-Rua, Utah State UniversityFollow
Kurt Wedegaertner, Utah State UniversityFollow
Brent Black, Utah State UniversityFollow
Brennan Bean, Utah State UniversityFollow
Burdette Barker, Utah State UniversityFollow
Matt Yost, Utah State UniversityFollow

Document Type

Article

Author ORCID Identifier

Alfonso Torres-Rua  https://orcid.org/0000-0002-2238-9550  

Kurt Wedegaertner  https://orcid.org/0009-0003-4803-9131  

Brent Black  https://orcid.org/0000-0003-0394-9206  

Brennan Bean  https://orcid.org/0000-0002-2853-0455  

Burdette Barker  https://orcid.org/0000-0002-5100-4971 

Matt Yost  https://orcid.org/0000-0001-5012-8481  

Journal/Book Title/Conference

Remote Sensing

Volume

18

Issue

6

Publisher

MDPI AG

Publication Date

3-10-2026

Journal Article Version

Version of Record

First Page

1

Last Page

36

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). Six equations were generated for different data-availability scenarios and validated using a leave-one-tree-out (modified k-fold) approach, resulting in Ψ_stem estimates with R² values ranging from 0.67 to 0.80 and root mean square errors (RMSE) ranging from 0.11 to 0.08 MPa. Notably, SR was able to produce interpretable equations that enhance model transparency and transferability. Model robustness was further confirmed using an independent dataset from a different location. To our knowledge, this is the first application of SR for Ψ_stem estimation, offering a scalable and interpretable tool to support irrigation management in tart cherry orchards.

Recommended Citation

Safre, A.L.S.; Torres-Rua, A.; Wedegaertner, K.; Black, B.; Bean, B.; Barker, B.; Yost, M. Predicting Tart Cherry Stem Water Potential Using UAV Multispectral Imagery and Environmental Data via Symbolic Regression. Remote Sens. 2026, 18, 853. https://doi.org/10.3390/rs18060853