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Comprehensive review on 3D point cloud segmentation in plants

文献类型： 外文期刊

作者： Song, Hongli 1 ; Wen, Weiliang 1 ; Wu, Sheng 1 ; Guo, Xinyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

3.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Peoples R China

关键词： Plant; Three-dimensional; Point cloud; Segmentation; Multi-scale; Deep learning

期刊名称：ARTIFICIAL INTELLIGENCE IN AGRICULTURE （ 影响因子：12.4； 五年影响因子：12.7 ）

ISSN： 2097-2113

年卷期： 2025 年 15 卷 2 期

页码：

收录情况： SCI

摘要： Segmentation of three-dimensional (3D) point clouds is fundamental in comprehending unstructured structural and morphological data. It plays a critical role in research related to plant phenomics, 3D plant modeling, and functional-structural plant modeling. Although technologies for plant point cloud segmentation (PPCS) have advanced rapidly, there has been a lack of a systematic overview of the development process. This paper presents an overview of the progress made in 3D point cloud segmentation research in plants. It starts by discussing the methods used to acquire point clouds in plants, and analyzes the impact of point cloud resolution and quality on the segmentation task. It then introduces multi-scale point cloud segmentation in plants. The paper summarizes and analyzes traditional methods for PPCS, including the global and local features. This paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised, unsupervised, and integrated approaches. It also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based, voxel-based, and point-based approaches respectively. Finally, the development of PPCS is discussed and prospected. Deep learning methods are predicted to become dominant in the field of PPCS, and 3D point cloud segmentation would develop towards more automated with higher resolution and precision. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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