A cotton organ segmentation method with phenotypic measurements from a point cloud using a transformer.

Abstract

Cotton phenomics plays a crucial role in understanding and managing the growth and development of cotton plants. The segmentation of point clouds, a process that underpins the measurement of plant organ structures through 3D point clouds, is necessary for obtaining precise phenotypic parameters. This study proposes a cotton point cloud organ semantic segmentation method named TPointNetPlus, which combines PointNet++ and Transformer algorithms. Firstly, a dedicated point cloud dataset for cotton plants is constructed using multi-view images. Secondly, the attention module Transformer is introduced into the PointNet++ model to increase the accuracy of feature extraction. Finally, organ-level cotton plant point cloud segmentation is performed using the HDBSCAN algorithm, successfully segmenting cotton leaves, bolls, and branches from the entire plant, and obtaining their phenotypic feature parameters. The research results indicate that the TPointNetPlus model achieved a high accuracy of 98.39% in leaf semantic segmentation. The correlation coefficients between the measured values of four phenotypic parameters (plant height, leaf area, and boll volume) ranged from 0.95 to 0.97, demonstrating the accurate predictive capability of the model for these key traits. The proposed method, which enables automated data analysis from a plant's 3D point cloud to phenotypic parameters, provides a reliable reference for in-depth studies of plant phenotypes.

Plant phenotyping relevance

綿花の3D点群から器官を分割し、草丈・葉面積・莢体積などの表現型形質を自動抽出する手法を開発・検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper describes a cotton point cloud dataset (Cotton3D) and a TPointNetPlus segmentation method, but the supplied blocks contain no public deposit, availability statement, or authors' URL for the dataset, code, or trained model. The only URL present is the Creative Commons license link. Supplementary material is un

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