植物三维点云分割

doi:10.3969/j.issn.0255-8297.2021.04.013

应用科学学报 ›› 2021, Vol. 39 ›› Issue (4): 660-671.doi: 10.3969/j.issn.0255-8297.2021.04.013

• CCF NCCA 2020专辑 • 上一篇

植物三维点云分割

赖亦斌¹, 陆声链^1,2, 钱婷婷³, 宋真¹, 陈明^1,2

1. 广西师范大学计算机科学与工程学院, 广西桂林 541004;
2. 广西师范大学广西多源信息挖掘与安全重点实验室, 广西桂林 541004;
3. 上海市农业科学院农业科技信息研究所, 上海 201403

收稿日期:2020-09-06 发布日期:2021-08-04
通信作者: 陆声链,副研究员,研究方向为图形图像处理、人工智能。E-mail:shllu@126.com E-mail:shllu@126.com
基金资助:
国家自然科学基金（No.61762013）；上海市科技兴农重点攻关项目基金（No.G2015060402）；广西教育厅项目基金（No.2018KY0078）资助

Three-Dimensional Point Cloud Segmentation for Plants

LAI Yibin¹, LU Shenglian^1,2, QIAN Tingting³, SONG Zhen¹, CHEN Ming^1,2

1. College of Computer Science and Technology, Guangxi Normal University, Guilin 541004, Guangxi, China;
2. Guangxi Key Lab of Multi-source Information Mining & Security, Guilin 541004, Guangxi, China;
3. Institute of Agricultural Science and Technology Information, Shanghai Academy of Agriculture Sciences, Shanghai 201403, China

Received:2020-09-06 Published:2021-08-04

摘要/Abstract

摘要： 针对植物点云具有形状不规则、密度不均匀的特点，提出一种适用于植物的三维点云分割方法。将烟草、玉米、黄瓜这3种植物作为样本数据，以滤波等预处理方法去除离群点与背景点，以欧氏聚类算法分割植物群体，并用区域增长算法、边缘提取算法、超体素聚类算法以及基于凹凸性的方法来分割叶片器官。将所提出的方法用于分割烟草、玉米的三维点云，其覆盖率分别为87.5%、96.9%，从而验证了该方法的可行性与有效性，为自动提取作物叶器官表型研究提供了线索。

关键词: 三维点云, 植物表型, 点云分割, 叶片分割

Abstract: Aiming at the irregular shape and uneven density of plant point clouds, a three-dimensional point cloud segmentation method applied to plants is proposed. Three plants of tobacco, corn, and cucumber are used as sample data, in which outliers and background points are removed by filtering and other preprocessing methods. Plant population is segmented by the Euclidean clustering algorithm, and leaf organs are segmented by region growing algorithm, edge extraction algorithm, super voxel clustering algorithm, and segmentation algorithm based on unevenness. The proposed method is used to segment three-dimensional point clouds of tobacco and corn, and the coverage rates are 87.5% and 96.9%, respectively. This verifies the feasibility and effectiveness of the method and provides clues for the automatic extraction of plant leaf organ phenotypes.

Key words: three-dimensional point cloud, plant phenotypic, point cloud segmentation, leaf segmentation

中图分类号:

TP391

赖亦斌, 陆声链, 钱婷婷, 宋真, 陈明. 植物三维点云分割[J]. 应用科学学报, 2021, 39(4): 660-671.

LAI Yibin, LU Shenglian, QIAN Tingting, SONG Zhen, CHEN Ming. Three-Dimensional Point Cloud Segmentation for Plants[J]. Journal of Applied Sciences, 2021, 39(4): 660-671.

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植物三维点云分割

Three-Dimensional Point Cloud Segmentation for Plants

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