Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds

To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality....

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Autores principales:	YANG Xu, HU Songtao, WANG Yinghua, YANG Wanneng, ZHAI Ruifang
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	cotton phenotypic traits lidar stem extraction leaf segmentation point cloud registration 3d phenotyping Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/d081c8cf1c274dc0b7dd44aee25db7af
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spelling	oai:doaj.org-article:d081c8cf1c274dc0b7dd44aee25db7af2021-11-17T07:52:00ZCotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds2096-809410.12133/j.smartag.2021.3.1.202102-SA003https://doaj.org/article/d081c8cf1c274dc0b7dd44aee25db7af2021-03-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-1-51.shtmlhttps://doaj.org/toc/2096-8094To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality. Therefore, the efficient, accurate, and non-destructive measurement of crop phenotypic traits, and the dynamic quantification of phenotypic traits are urgently needed for crop phenotypic research, and breeding as well as for modern agricultural development. In this study, cotton plants were taken as research objects, and the multi-temporal point cloud data of cotton plants were collected by using three-dimensional laser scanning technology. The multi-temporal point clouds of three cotton plants at four time points were collected. First, RANSAC algorithm was implemented for main stem extraction on the original point cloud data of cotton plants, then region growing based clustering was carried out for leaf segmentation. Plant height was estimated by calculating the end points of the segmented main stem. Leaf length and width measurements were conducted on the segmented leaf parts. In addition, the volume was also estimated through the convex hull of the original point cloud of plant cotton. Then, multi-temporal point clouds of plants were registered, and organ correspondence was constructed with the Hungarian method. Finally, dynamic quantification of phenotypic traits including plant volume, plant height, leaf length, leaf width, and leaf area were calculated and analyzed. The overall performance of the approaches achieved a matching rate through a series of experiments, and the traits extracted by using of point cloud showed high correlation with the manually measured ones. The relative error between plant height and manual measurement results did not exceed 1.0%. The estimated leaf length and width on point clouds were highly correlated with the manually measured ones, and the coefficient of determination was nearly 1.0. The proposed 3D phenotyping methodology can be introduced and used to other crops for phenotyping.YANG XuHU SongtaoWANG YinghuaYANG WannengZHAI RuifangEditorial Office of Smart Agriculturearticlecotton phenotypic traitslidarstem extractionleaf segmentationpoint cloud registration3d phenotypingAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 1, Pp 51-62 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	cotton phenotypic traits lidar stem extraction leaf segmentation point cloud registration 3d phenotyping Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	cotton phenotypic traits lidar stem extraction leaf segmentation point cloud registration 3d phenotyping Agriculture (General) S1-972 Technology (General) T1-995 YANG Xu HU Songtao WANG Yinghua YANG Wanneng ZHAI Ruifang Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds
description	To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality. Therefore, the efficient, accurate, and non-destructive measurement of crop phenotypic traits, and the dynamic quantification of phenotypic traits are urgently needed for crop phenotypic research, and breeding as well as for modern agricultural development. In this study, cotton plants were taken as research objects, and the multi-temporal point cloud data of cotton plants were collected by using three-dimensional laser scanning technology. The multi-temporal point clouds of three cotton plants at four time points were collected. First, RANSAC algorithm was implemented for main stem extraction on the original point cloud data of cotton plants, then region growing based clustering was carried out for leaf segmentation. Plant height was estimated by calculating the end points of the segmented main stem. Leaf length and width measurements were conducted on the segmented leaf parts. In addition, the volume was also estimated through the convex hull of the original point cloud of plant cotton. Then, multi-temporal point clouds of plants were registered, and organ correspondence was constructed with the Hungarian method. Finally, dynamic quantification of phenotypic traits including plant volume, plant height, leaf length, leaf width, and leaf area were calculated and analyzed. The overall performance of the approaches achieved a matching rate through a series of experiments, and the traits extracted by using of point cloud showed high correlation with the manually measured ones. The relative error between plant height and manual measurement results did not exceed 1.0%. The estimated leaf length and width on point clouds were highly correlated with the manually measured ones, and the coefficient of determination was nearly 1.0. The proposed 3D phenotyping methodology can be introduced and used to other crops for phenotyping.
format	article
author	YANG Xu HU Songtao WANG Yinghua YANG Wanneng ZHAI Ruifang
author_facet	YANG Xu HU Songtao WANG Yinghua YANG Wanneng ZHAI Ruifang
author_sort	YANG Xu
title	Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds
title_short	Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds
title_full	Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds
title_fullStr	Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds
title_full_unstemmed	Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds
title_sort	cotton phenotypic trait extraction using multi-temporal laser point clouds
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/d081c8cf1c274dc0b7dd44aee25db7af
work_keys_str_mv	AT yangxu cottonphenotypictraitextractionusingmultitemporallaserpointclouds AT husongtao cottonphenotypictraitextractionusingmultitemporallaserpointclouds AT wangyinghua cottonphenotypictraitextractionusingmultitemporallaserpointclouds AT yangwanneng cottonphenotypictraitextractionusingmultitemporallaserpointclouds AT zhairuifang cottonphenotypictraitextractionusingmultitemporallaserpointclouds
_version_	1718425891420241920

Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds

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