Robotic mapping of soil volumetric water content with geospatial soil apparent electrical conductivity in micro-irrigated citrus orchards in California

Authors: MORBIDINI, FRANCESCO - University Of California, Riverside; SAMANTA, ANITRA - University Of California, Riverside; MAUCIERI, CARMELO - University Of Padua; KARYDIS, KOSTANTINOS - University Of California, Riverside; MAUK, PEGGY - University Of California, Riverside; Skaggs, Todd; SCUDIERO, ELIA - University Of California, Riverside

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/5/2026
Publication Date: 2/11/2026
Citation: Morbidini, F., Samanta, A., Maucieri, C., Karydis, K., Mauk, P.A., Skaggs, T.H., Scudiero, E. 2026. Robotic mapping of soil volumetric water content with geospatial soil apparent electrical conductivity in micro-irrigated citrus orchards in California. Computers and Electronics in Agriculture. 245:11540. https://doi.org/10.1016/j.compag.2026.111540.
DOI: https://doi.org/10.1016/j.compag.2026.111540

Interpretive Summary: Improved irrigation management is possible when a farmer has accurate information about the soil water content of a field. Monitoring soil water content across a field is expensive and time-consuming using conventional methods. This study tested an innovative semi-autonomous robotic platform equipped with a lightweight soil sensor to map soil water content in two California citrus orchards. Results showed that reliable water predictions could be achieved with relatively minimal calibration, thus demonstrating that robotic surveys provide a practical and scalable approach for precision soil moisture mapping in drip-irrigated orchards. The research will be of interest to industry professionals seeking to improve the efficiency and profitability of irrigated farmland.

Technical Abstract: Soil moisture plays a crucial role in irrigation management and in understanding key hydrological and agronomic processes. This study evaluated an innovative approach to estimate soil volumetric water content (WC) using apparent electrical conductivity (ECa) measured by a portable electromagnetic induction sensor mounted on a semi-autonomous ground robot. The investigation was conducted between October 2024 and March 2025, in two California citrus orchards, each surveyed four times. Geospatial EMI data were acquired across the entire fields. The WC ground-truth measurements were collected using a time domain reflectometry sensor at twenty 0.5×0.5 m footprints per orchard. The ECa data was calibrated to estimate WC with analysis of covariance regression. Different model formulations were used to investigate the WC prediction errors due to varying model inputs and size of the ground-truth sample (N=2, 3, 4, 5, 6, 8, 10, and 12). All models were calibrated and evaluated (3 data points per field) using randomly selected calibration and evaluation data points 10,000 times. With minimal ground-truth (N=2), the average evaluation root mean square errors (RMSEs) ranged between 0.050 and 0.066 m^3/m^3 across the tested models. Accuracy improved (RMSEs ranging between 0.041 and 0.052 m^3/m^3) with more calibration points up to N=6, beyond which improvements became marginal. This research advanced the field of WC sensing in precision agriculture by combining robotic ECa measurements and data driven modeling using minimal-ground truth to derive accurate WC estimations. Researchers, growers, and practitioners may employ this approach to obtain WC maps to improve irrigation management at the orchard scale.