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.
Highlights
Image-based approaches to rapidly and nondestructively measure plant morphological traits have emerged and quickly developed in response to the need for accelerating high-throughput plant phenotyping that would eventually enable effective use of genomic data to bridge the genotype-to-phenotype gap for crop improvement [1,2]
We report the development of a novel LiDAR-based sensing system that can automatically produce 3D point clouds of plants with a 360-degree view
We have developed a fully integrated data processing pipeline to reconstruct plant leaf surfaces and derive leaf area, leaf inclination angle, and leaf angular distribution
Summary
Image-based approaches to rapidly and nondestructively measure plant morphological traits have emerged and quickly developed in response to the need for accelerating high-throughput plant phenotyping that would eventually enable effective use of genomic data to bridge the genotype-to-phenotype gap for crop improvement [1,2]. Voxelization: Raw point cloud data are logged in a 2D matrix where every row corresponds to the Clustering and segmentation: The pot and the stem were removed from the data by filtering the points within a suitable radius from the center. This step made the leaves to be spatially separated from each other. K-means clustering was used to segment the points that belonged to each leaf by feeding the k-value as the number of leaves in the plant [27]. The process was repeated for all the segmented leaves in a plant
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