A Single-Operator Push-Cart Multi-Beam LiDAR Platform for Multi-Trait Field Phenotyping

Area of Science:

• Agricultural engineering
• Remote sensing technology
• Plant science

Background:

• Accurate measurement of plant biomass and canopy structure is crucial for agricultural research and management.
• Existing remote sensing methods may have limitations in canopy penetration and detailed trait assessment.

Purpose of the Study:

• To present a novel, single-operator, push-cart LiDAR platform for plot-level canopy analysis.
• To validate biomass estimation and assess the efficacy of multi-angle scanning for improved canopy penetration.
• To expand the suite of LiDAR-derived traits for detailed plot-level characterization.

Main Methods:

• Development of a 16-beam LiDAR push-cart system with automated data acquisition and processing.
• Validation of biomass estimation in hairy vetch and corn using thinning experiments.
• Comparison of perpendicular and multi-angle LiDAR beam scanning in corn canopies.
• Application of persistent homology and ray-tracing for advanced canopy trait derivation (LAI, MTA, stand density, FAD).

Main Results:

• Voxelized plant volume strongly correlated with biomass in vetch (r² = 0.88).
• Multi-angle LiDAR beams significantly improved upper canopy voxel counts compared to perpendicular scans in corn (p < 0.001).
• Persistent homology distinguished between leaf and whole-plant removal treatments (p = 0.0039).
• Plot-bounded foliage area density (FAD) was more sensitive to plot treatments than LAI or MTA.

Conclusions:

• The portable, multi-beam LiDAR cart provides robust and comparable plot-level canopy measurements.
• Multi-angle scanning enhances LiDAR's ability to penetrate dense upper canopies.
• Advanced LiDAR-derived traits like FAD offer improved sensitivity for detecting treatment effects in agricultural plots.