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Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as st...

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Autores principales: He, Wei, Ye, Zhihao, Li, Mingshuang, Yan, Yulu, Lu, Wei, Xing, Guangnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407792/
https://www.ncbi.nlm.nih.gov/pubmed/37560031
http://dx.doi.org/10.3389/fpls.2023.1181322
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author He, Wei
Ye, Zhihao
Li, Mingshuang
Yan, Yulu
Lu, Wei
Xing, Guangnan
author_facet He, Wei
Ye, Zhihao
Li, Mingshuang
Yan, Yulu
Lu, Wei
Xing, Guangnan
author_sort He, Wei
collection PubMed
description Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.
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spelling pubmed-104077922023-08-09 Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN He, Wei Ye, Zhihao Li, Mingshuang Yan, Yulu Lu, Wei Xing, Guangnan Front Plant Sci Plant Science Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves. Frontiers Media S.A. 2023-07-25 /pmc/articles/PMC10407792/ /pubmed/37560031 http://dx.doi.org/10.3389/fpls.2023.1181322 Text en Copyright © 2023 He, Ye, Li, Yan, Lu and Xing 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
He, Wei
Ye, Zhihao
Li, Mingshuang
Yan, Yulu
Lu, Wei
Xing, Guangnan
Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN
title Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN
title_full Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN
title_fullStr Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN
title_full_unstemmed Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN
title_short Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN
title_sort extraction of soybean plant trait parameters based on sfm-mvs algorithm combined with grnn
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407792/
https://www.ncbi.nlm.nih.gov/pubmed/37560031
http://dx.doi.org/10.3389/fpls.2023.1181322
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