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

• 3D sensing technologies were used to assess oak tree maturity and determine agrochemical requirements • RGB-D-guided point cloud processing enabled precise oak trunk diameter estimation. • FastSAM segmentation and circle fitting improved accuracy in measuring trunk diameter. • Canopy volume estimation using LiDAR data for targeted agrochemical application. • Integrated sensing approach supports automated nursery assessments and promotes data-driven, sustainable production. Effective assessment of tree maturity and agrochemical application requirements is important for optimizing resource use and sustainability in woody ornamental nurseries. This study integrates a ground-based red, green, blue – depth (RGB-D) camera and Light Detection and Ranging (LiDAR) system 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 meters (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, ROI (region of interest) 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 results in Plot-1 exhibited an average absolute error percentage of 0.23%, with an RMSE (root mean square error) of 0.03 meters and MAE (mean average error) of 0.02 meters, whereas Plot-2 showed an error percentage of 1.11%, with an RMSE of 0.08 meters and MAE of 0.07 meters. 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.