Assessing ornamental tree maturity and spray requirements using depth sensing and LiDAR technologies

Abstract

Effective assessment of tree maturity and agrochemical application requirements is important for optimizing resource use and sustainability in woody ornamental nurseries. This study utilized red, green, blue – depth (RGB-D) camera and light detection and ranging (LiDAR) sensor technologies to measure key physiological parameters, trunk diameter and canopy volume, for maturity evaluation and precision spraying, respectively. Trunk diameter was calculated using a circle-fitting algorithm on point clouds at 0.15 m (6 inches) above ground, derived from RGB-D pair segmented using Fast Segment Anything Model (FastSAM). Canopy volume was estimated by using a convex hull algorithm on processed point clouds through point cloud registration, region of interest (ROI) clipping, and denoising. Thirty-two trees were randomly selected in pairs from two plots (Plot-1 and Plot-2) with varying terrains for this experiment. The trunk diameter measurement results in Plot-1 exhibited an average absolute error percentage of 0.23 %, with an root mean square error (RMSE) of 0.03 m and mean average error (MAE) of 0.02 m, whereas Plot-2 showed an error percentage of 1.11 %, with an RMSE of 0.08 m and MAE of 0.07 m. The trunk diameter was further analyzed for tree maturity analysis, revealing that Plot-1 had 10 mature trees while Plot-2 had only 5, indicating a more advanced growth stage in Plot-1. This classification was validated against manual assessments, showing 100 % agreement across all 32 experimental trees, confirming the accuracy of the RGB-D system in determining tree maturity. Similarly, results for the canopy volume of Plot-1 indicated an average absolute error percentage of 10.99 %, with RMSE and MAE values of 0.37 cubic meters and 0.33 cubic meters, respectively, while Plot-2 showed an error percentage of 13.01 %, with an RMSE of 0.27 cubic meters and MAE of 0.24 cubic meters. These results demonstrate the feasibility and accuracy of integrating LiDAR and RGB-D technologies for efficient nursery management, supporting maturity assessment and precision agrochemical application as part of sustainable practices in ornamental horticulture.