Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards

TL;DR

This paper introduces a real-time, adaptive system for estimating individual tree canopy volumes using mobile LiDAR data during routine orchard navigation, effectively handling diverse orchard structures.

Contribution

It presents a novel integrated pipeline combining LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction for accurate, real-time canopy volume estimation in varied orchard environments.

Findings

Achieved 93% segmentation success in pistachio orchard

Achieved 80% segmentation success in almond orchard

Strong correlation with drone-based volume estimates

Abstract

We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation. Unlike prior approaches that rely on static scans or assume uniform orchard structures, our method adapts to varying field geometries via an integrated pipeline of LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction. We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns. A hybrid clustering strategy combining DBSCAN and spectral clustering enables robust per-tree segmentation, achieving 93% success in pistachio and 80% in almond, with strong agreement to drone derived canopy volume estimates. This work advances scalable, non-intrusive tree monitoring for structurally diverse orchard environments.