Calculation method of canopy effective volume for fruit tree based on LiDAR point cloud data.

Area of Science:

• Agricultural Engineering
• Remote Sensing
• Forestry

Background:

• Accurate fruit tree canopy volume is crucial for precise orchard management.
• Existing methods overestimate volume by failing to account for canopy porosity.
• LiDAR point cloud data offers potential for detailed canopy analysis.

Purpose of the Study:

• To develop a novel method for calculating fruit tree canopy effective volume (EV) using LiDAR point cloud data.
• To accurately exclude canopy porosity in volume estimations.
• To provide a foundation for enhanced precision agriculture and orchard management strategies.

Main Methods:

• Reconstruction of fruit tree canopy models using an improved alpha-shape algorithm.
• Development of a canopy effective volume coefficient.
• Calculation of EV as the product of reconstructed volume and the coefficient.
• Validation using simulated and real-world orchard experiments.

Main Results:

• The proposed EV method achieved high accuracy with R² of 0.9720.
• Optimized parameters include voxel size equal to average nearest neighbor distance and partition size five times the voxel size.
• Significant volume reduction rates compared to ASBS (0.5101), CHBS (0.6953), and VB (0.6213) methods were observed.

Conclusions:

• The EV method accurately quantifies canopy effective volume by removing porosity.
• This approach offers improved decision support for precise orchard management.
• LiDAR-based effective volume calculation is a viable advancement for agricultural applications.