An Efficient Processing Approach for Colored Point Cloud-Based High-Throughput Seedling Phenotyping

Abstract

Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.

Plant phenotyping relevance

RGB-D点群を用いて個体ごとの草丈・葉面積を抽出する高スループット表現型計測パイプラインを開発し、異なる生育条件・種で検証しており、表現型取得手法が中心である。

Code and data availability

The supplied blocks describe a Kinect v2-based seedling point cloud phenotyping pipeline, but contain no public dataset, image, or code deposit. No data or code availability statement appears, and no author-provided public URL is present (allowed_urls is empty).

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