Oblique Trellised Apple Orchard Photogrammetry for Early Phenology Tracking of Individual Plants

Precision agricultural monitoring is now widely deployed in flat annual croplands, and yet these same methods and data outputs fail to capture the complexity of 3D, vertical crop-yielding surfaces such as fruit orchards. This study presents a novel and cost-effective drone photogrammetry approach that employs consumer-grade equipment and production-ready software to inform orchard management decisions.Flying at unconventionally low altitudes (12-30 m), we employ oblique flight patterns to capture vertical surfaces of trellised apple trees. Using precisely georeferenced image stacks, we construct high-resolution photogrammetry point clouds that allow us to quantify orchard management targets such as flowers and fruitlets. This allows us to adapt forest ecology analysis methods (e.g. lidR package [1]) to an agricultural setting, and to achieve the equivalent of 3 mm "Ground Sampling Distance" (GSD) with industry-standard 1-2 cm georeferencing accuracy. Field experiments in South Tyrol’s Vinschgau-Venosta apple orchards during critical flowering periods (April 2025) demonstrated next-day "bloom density" mapping capabilities. Workflows yielding aligned outputs were validated on a different drone and multispectral payload to detect 20-40 mm fruitlets (June-July 2025). Our approach bridges the gap in spatial and temporal resolution between more mature and spatially extensive 2D drone photogrammetry and slower but more precise ground-based approaches, and is well-suited for spatially complex agricultural systems. Overall, we show that drone-based monitoring can reduce labor and fuel inputs needed for tree-level monitoring in trellised orchard systems with minimal capital investment (i.e., equipment costs significantly below standard farm machinery).