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.

Purpose: Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. Methods: Soil apparent electrical conductivity (ECa) was mapped in spring 2022 across four commercial tart cherry blocks (8.5–10.5 ha; approximately 3,500 trees per block), followed by canopy sensing in 2023–2024. Canopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry. Spatial layers were aligned to per-tree grid cells. An August 2025 campaign compared UAV- and LiDAR-derived tree height with ground-truthed height. Results: Soil-to-canopy relationships were weak to moderate but consistent within blocks ( r = 0.10–0.40), with strength and direction varying by site conditions. Canopy density was more strongly associated with UAV-derived volume than height. UAV-derived 90th-percentile height best predicted ground-truthed height ( R ² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error ( R ² = 0.70; RMSE = 0.52 m). Cross-sensor agreement was moderate to strong ( r = 0.41–0.65). Per-tree rankings were stable between years for UAV height and volume. Conclusion: Whole-block sensing revealed persistent spatial patterns that could support management-zone delineation. UAV photogrammetry provided accurate canopy metrics, MTLS offered measurements suited to routine orchard operations, ceptometry added seasonal canopy-density information, and ECa provided soil context. Occasional ECa mapping combined with strategically timed UAV surveys and other sensors as needed could reduce redundant sensing while supporting fertilizer evaluation, pruning, and labor allocation.

Trunk diameter is a fundamental structural trait used to quantify tree size, vigor, and long-term growth in orchard systems, yet it remains difficult to measure efficiently at orchard scale. This study developed and validated a low-cost mobile imaging system that integrates RGB-D cameras, object detection, deep-learning segmentation, and driveline-based digital odometry to estimate trunk diameter and map structural variation across tart cherry (Prunus cerasus) orchards. Trunks were detected using a YOLO-based model, segmented with a SAM-derived mask, and reconstructed in 3D from depth data to extract real-world diameter at 40 cm above the graft union. A variable-reluctance sensor, mounted on the transmission output, provided odometry-based distance measurements for spatial mapping under canopy conditions where GNSS performance was unreliable. In a 12-year-old, 1 ha experimental orchard, in Utah, USA, trunk diameter estimates showed strong agreement with manual measurements when unobstructed views were available, with a mean absolute error (MAE) of 0.95 cm, root mean square error of 1.16 cm, and R2 of 0.79. Across all 462 trees, automated per-tree averaging produced an MAE of 1.40 cm. In a 14-year-old, 9.5 ha commercial orchard, trunk diameter patterns aligned with UAV-derived canopy height, reflecting underlying zones of tree vigor. These results demonstrate that mobile RGB-D imaging combined with driveline-based odometry can reliably estimate trunk diameter at orchard scale, supporting practical, cost-effective structural mapping for precision horticulture.