Work

Advanced monitoring of almond orchard water status using machine learning and remote sensing

Year: 2025

Type: article

Abstract: Water management is essential for optimizing yields in almond orchards, particularly in regions with limited water resources. This study integrates satellite imagery, meteorological data, soil data, and machine learning techniques to monitor and predict the spatial variability of stem water potential (SWP) in an almond orchard in Corning, California, United States. A random forest (RF) model was developed using weekly SWP measurements collected from a pressure chamber between 2019 and 2021 at 24 monitoring locations. Predictor variables included meteorological variables (air temperature, solar radiation, wind speed, relative humidity, vapor pressure deficit), soil parameters (soil bulk density and soil water content), and vegetation indices derived from satellite imagery (normalized difference vegetation index) and the evaporation fraction. The RF model demonstrated high accuracy in predicting SWP, with a Nash-Sutcliffe Efficiency (NSE) of 0.91 and a root mean square error (RMSE) of 0.17 MPa, as evidenced by the goodness-of-fit evaluation. The cumulative probability plot indicated that 78.2% of the NSE values fall within the "very good" range and 21.8% within the "good" range, underscoring the model's reliability. It was found that the key variables for SWP prediction are air temperature, evaporation fraction, wind speed, and solar radiation. Spatial maps generated with the high-resolution aerial imagery by the model revealed significant within-field variability, particularly during critical growth stages such as the hull split period in July, where high SWP values indicated water stress due to deficit irrigation. These maps highlighted areas of the orchard experiencing greater water stress, guiding more precise irrigation management. This study highlights the importance of integrating high-resolution remote sensing data with machine learning algorithms to enhance water management practices in almond orchards. The findings suggest that the spatial and temporal predictions of SWP using the RF model can support precise irrigation scheduling, leading to improved water use efficiency and sustainability in almond production.

Source: Scientia Horticulturae

DOI: 10.1016/j.scienta.2025.114020

Authors: Srinivasa Rao Peddinti, Isaya Kisekka

Institution: University of California, Davis

Language: English

Topic: Irrigation Practices and Water Management

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,000