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Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera
INTRODUCTION: Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the f...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911875/ https://www.ncbi.nlm.nih.gov/pubmed/36778701 http://dx.doi.org/10.3389/fpls.2023.1097725 |
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author | Song, Peng Li, Zhengda Yang, Meng Shao, Yang Pu, Zhen Yang, Wanneng Zhai, Ruifang |
author_facet | Song, Peng Li, Zhengda Yang, Meng Shao, Yang Pu, Zhen Yang, Wanneng Zhai, Ruifang |
author_sort | Song, Peng |
collection | PubMed |
description | INTRODUCTION: Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the field suitable for various crops by using a consumer-grade RGB-D camera installed on a ground-based movable platform, which can collect RGB images as well as depth images of crop canopy sequences dynamically. METHODS: A scale-invariant feature transform (SIFT) operator was used to detect adjacent date frames acquired by the RGB-D camera to calculate the point cloud alignment coarse matching matrix and the displacement distance of adjacent images. The data frames used for point cloud matching were selected according to the calculated displacement distance. Then, the colored ICP (iterative closest point) algorithm was used to determine the fine matching matrix and generate point clouds of the crop row. The clustering method was applied to segment the point cloud of each plant from the crop row point cloud, and 3D phenotypic traits, including plant height, leaf area and projected area of individual plants, were measured. RESULTS AND DISCUSSION: We compared the effects of LIDAR and image-based 3D reconstruction methods, and experiments were carried out on corn, tobacco, cottons and Bletilla striata in the seedling stage. The results show that the measurements of the plant height (R²= 0.9~0.96, RSME = 0.015~0.023 m), leaf area (R²= 0.8~0.86, RSME = 0.0011~0.0041 m (2) ) and projected area (R² = 0.96~0.99) have strong correlations with the manual measurement results. Additionally, 3D reconstruction results with different moving speeds and times throughout the day and in different scenes were also verified. The results show that the method can be applied to dynamic detection with a moving speed up to 0.6 m/s and can achieve acceptable detection results in the daytime, as well as at night. Thus, the proposed method can improve the efficiency of individual crop 3D point cloud data extraction with acceptable accuracy, which is a feasible solution for crop seedling 3D phenotyping outdoors. |
format | Online Article Text |
id | pubmed-9911875 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99118752023-02-11 Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera Song, Peng Li, Zhengda Yang, Meng Shao, Yang Pu, Zhen Yang, Wanneng Zhai, Ruifang Front Plant Sci Plant Science INTRODUCTION: Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the field suitable for various crops by using a consumer-grade RGB-D camera installed on a ground-based movable platform, which can collect RGB images as well as depth images of crop canopy sequences dynamically. METHODS: A scale-invariant feature transform (SIFT) operator was used to detect adjacent date frames acquired by the RGB-D camera to calculate the point cloud alignment coarse matching matrix and the displacement distance of adjacent images. The data frames used for point cloud matching were selected according to the calculated displacement distance. Then, the colored ICP (iterative closest point) algorithm was used to determine the fine matching matrix and generate point clouds of the crop row. The clustering method was applied to segment the point cloud of each plant from the crop row point cloud, and 3D phenotypic traits, including plant height, leaf area and projected area of individual plants, were measured. RESULTS AND DISCUSSION: We compared the effects of LIDAR and image-based 3D reconstruction methods, and experiments were carried out on corn, tobacco, cottons and Bletilla striata in the seedling stage. The results show that the measurements of the plant height (R²= 0.9~0.96, RSME = 0.015~0.023 m), leaf area (R²= 0.8~0.86, RSME = 0.0011~0.0041 m (2) ) and projected area (R² = 0.96~0.99) have strong correlations with the manual measurement results. Additionally, 3D reconstruction results with different moving speeds and times throughout the day and in different scenes were also verified. The results show that the method can be applied to dynamic detection with a moving speed up to 0.6 m/s and can achieve acceptable detection results in the daytime, as well as at night. Thus, the proposed method can improve the efficiency of individual crop 3D point cloud data extraction with acceptable accuracy, which is a feasible solution for crop seedling 3D phenotyping outdoors. Frontiers Media S.A. 2023-01-27 /pmc/articles/PMC9911875/ /pubmed/36778701 http://dx.doi.org/10.3389/fpls.2023.1097725 Text en Copyright © 2023 Song, Li, Yang, Shao, Pu, Yang and Zhai 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 Song, Peng Li, Zhengda Yang, Meng Shao, Yang Pu, Zhen Yang, Wanneng Zhai, Ruifang Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera |
title | Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera |
title_full | Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera |
title_fullStr | Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera |
title_full_unstemmed | Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera |
title_short | Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera |
title_sort | dynamic detection of three-dimensional crop phenotypes based on a consumer-grade rgb-d camera |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911875/ https://www.ncbi.nlm.nih.gov/pubmed/36778701 http://dx.doi.org/10.3389/fpls.2023.1097725 |
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