植物三维点云分割

doi:10.3969/j.issn.0255-8297.2021.04.013

Abstract

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

CLC Number:

TP391

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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Three-Dimensional Point Cloud Segmentation for Plants

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