Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation...

Area of Science:

• Agricultural Engineering
• Robotics
• Computer Vision

Background:

• Accurate measurement of orchard canopy parameters is crucial for effective management.
• Existing methods for canopy measurement can be labor-intensive and time-consuming.

Purpose of the Study:

• To develop a mobile scanning system for precise measurement of fruit tree canopy morphological parameters.
• To utilize 3D Simultaneous Localization and Mapping (SLAM) for orchard environment reconstruction.

Main Methods:

• A lightweight LiDAR-Inertial Measurement Unit (LiDAR-IMU) state estimator and rotation-constrained optimization for point cloud map reconstruction.
• Statistical Outlier Removal (SOR) filtering, European clustering, and k-nearest neighbour (KNN) search for point cloud segmentation and restoration.
• 3D alpha-shape algorithm and point cloud statistical methods for canopy height and volume calculation.

Main Results:

• The system accurately reconstructs orchard point cloud environments.
• Achieved 95.4% accuracy in fruit tree segmentation.
• High R² values (0.93682 for height, 0.8406 for volume) and low RMSE (0.04337 for height, 1.5738 for volume) for canopy parameter estimation.

Conclusions:

• The developed system provides accurate orchard crown information.
• Demonstrates significant application value for intelligent fruit tree measurement and precision agriculture.