A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Abstract

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R² > 0.91 for individual leaf area; R² > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.

Plant phenotyping relevance

LiDARによる3D植物形態形質の高スループット取得装置と解析パイプラインを開発し、基準法との検証も行っており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The article describes a LiDAR-based phenotyping instrument and processing pipeline for maize and sorghum, but no blocks contain any public dataset, point cloud/image deposit, or author code availability statement with a URL. The only URL present is the CC BY license notice, which is not a paper-specific asset.

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