HALF: Histogram of Angles in Linked Features for 3D Point Cloud Data Segmentation of Plants for Robust Sensing

Abstract

This paper presents a novel method, Histogram of Angles in Linked Features (HALF), designed for the segmentation of 3D point cloud data of plants for robust sensing. The proposed method leverages local angular features extracted from 3D measurements obtained via sensing technologies such as laser scanning, LiDAR, or photogrammetry. HALF enables efficient identification of plant structures-leaves, stems, and knots-without requiring large-scale labeled datasets, making it highly suitable for applications in plant phenotyping and structural analysis. To enhance robustness and interpretability, we extend HALF to a convolution-based mathematical framework and introduce the Sequential Competitive Segmentation Algorithm (SCSA) for phytomer-level classification. Experimental results using 3D point cloud data of soybean plants demonstrate the feasibility of our method in sensor-based plant monitoring systems. By providing a low-cost and efficient approach for plant structure analysis, HALF contributes to the advancement of sensor-driven plant phenotyping and precision agriculture.

Plant phenotyping relevance

植物の3D点群から葉・茎・節などの構造を分割・分類する新規センシング手法を開発し、植物フェノタイピングへの適用可能性を実証しているため。

Code and data availability

The paper's soybean 3D point cloud data and HALF/SCSA analysis code are not publicly released: the Data Availability Statement reads 'Not applicable,' and no repository, supplement, or public URL for the point cloud data, ground-truth labels, or author code is provided. The only URL mentioned (https://www.gene.affrc.go

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