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DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

The 3D point cloud data are used to analyze plant morphological structure. Organ segmentation of a single plant can be directly used to determine the accuracy and reliability of organ-level phenotypic estimation in a point-cloud study. However, it is difficult to achieve a high-precision, automatic,...

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Autores principales: Wang, Dabao, Song, Zhi, Miao, Teng, Zhu, Chao, Yang, Xin, Yang, Tao, Zhou, Yuncheng, Den, Hanbing, Xu, Tongyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9927642/
https://www.ncbi.nlm.nih.gov/pubmed/36798707
http://dx.doi.org/10.3389/fpls.2023.1109314
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author Wang, Dabao
Song, Zhi
Miao, Teng
Zhu, Chao
Yang, Xin
Yang, Tao
Zhou, Yuncheng
Den, Hanbing
Xu, Tongyu
author_facet Wang, Dabao
Song, Zhi
Miao, Teng
Zhu, Chao
Yang, Xin
Yang, Tao
Zhou, Yuncheng
Den, Hanbing
Xu, Tongyu
author_sort Wang, Dabao
collection PubMed
description The 3D point cloud data are used to analyze plant morphological structure. Organ segmentation of a single plant can be directly used to determine the accuracy and reliability of organ-level phenotypic estimation in a point-cloud study. However, it is difficult to achieve a high-precision, automatic, and fast plant point cloud segmentation. Besides, a few methods can easily integrate the global structural features and local morphological features of point clouds relatively at a reduced cost. In this paper, a distance field-based segmentation pipeline (DFSP) which could code the global spatial structure and local connection of a plant was developed to realize rapid organ location and segmentation. The terminal point clouds of different plant organs were first extracted via DFSP during the stem-leaf segmentation, followed by the identification of the low-end point cloud of maize stem based on the local geometric features. The regional growth was then combined to obtain a stem point cloud. Finally, the instance segmentation of the leaf point cloud was realized using DFSP. The segmentation method was tested on 420 maize and compared with the manually obtained ground truth. Notably, DFSP had an average processing time of 1.52 s for about 15,000 points of maize plant data. The mean precision, recall, and micro F1 score of the DFSP segmentation algorithm were 0.905, 0.899, and 0.902, respectively. These findings suggest that DFSP can accurately, rapidly, and automatically achieve maize stem-leaf segmentation tasks and could be effective in maize phenotype research. The source code can be found at https://github.com/syau-miao/DFSP.git.
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spelling pubmed-99276422023-02-15 DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot Wang, Dabao Song, Zhi Miao, Teng Zhu, Chao Yang, Xin Yang, Tao Zhou, Yuncheng Den, Hanbing Xu, Tongyu Front Plant Sci Plant Science The 3D point cloud data are used to analyze plant morphological structure. Organ segmentation of a single plant can be directly used to determine the accuracy and reliability of organ-level phenotypic estimation in a point-cloud study. However, it is difficult to achieve a high-precision, automatic, and fast plant point cloud segmentation. Besides, a few methods can easily integrate the global structural features and local morphological features of point clouds relatively at a reduced cost. In this paper, a distance field-based segmentation pipeline (DFSP) which could code the global spatial structure and local connection of a plant was developed to realize rapid organ location and segmentation. The terminal point clouds of different plant organs were first extracted via DFSP during the stem-leaf segmentation, followed by the identification of the low-end point cloud of maize stem based on the local geometric features. The regional growth was then combined to obtain a stem point cloud. Finally, the instance segmentation of the leaf point cloud was realized using DFSP. The segmentation method was tested on 420 maize and compared with the manually obtained ground truth. Notably, DFSP had an average processing time of 1.52 s for about 15,000 points of maize plant data. The mean precision, recall, and micro F1 score of the DFSP segmentation algorithm were 0.905, 0.899, and 0.902, respectively. These findings suggest that DFSP can accurately, rapidly, and automatically achieve maize stem-leaf segmentation tasks and could be effective in maize phenotype research. The source code can be found at https://github.com/syau-miao/DFSP.git. Frontiers Media S.A. 2023-01-31 /pmc/articles/PMC9927642/ /pubmed/36798707 http://dx.doi.org/10.3389/fpls.2023.1109314 Text en Copyright © 2023 Wang, Song, Miao, Zhu, Yang, Yang, Zhou, Den and Xu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Wang, Dabao
Song, Zhi
Miao, Teng
Zhu, Chao
Yang, Xin
Yang, Tao
Zhou, Yuncheng
Den, Hanbing
Xu, Tongyu
DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
title DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
title_full DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
title_fullStr DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
title_full_unstemmed DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
title_short DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
title_sort dfsp: a fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9927642/
https://www.ncbi.nlm.nih.gov/pubmed/36798707
http://dx.doi.org/10.3389/fpls.2023.1109314
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