Integrating soil and canopy sensing to map and relate variability in tart cherry orchards

Abstract

Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. 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. 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 (R² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error (R² = 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. 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. Multi-sensor integration: Combined soil apparent electrical conductivity, UAV photogrammetry, LiDAR, and mobile ceptometry to map within-orchard variability at commercial scale. Cross-sensor evaluation: Quantified relationships among soil, canopy structure, and canopy density; UAV canopy height (P90) most accurately predicted ground truth (R² = 0.89). Management relevance: Identified repeatable spatial patterns that could support zone-based, variable-rate, and labor-efficient management strategies in tart cherry orchards. This study demonstrates how integrating mapped soil apparent electrical conductivity, UAV photogrammetry, LiDAR, and mobile ceptometry can quantify within-orchard variability at commercial scale. Linking soil and canopy information provides a practical framework for identifying persistent spatial patterns that could support management-zone delineation and guide variable-rate or labor-efficient orchard practices.