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Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply...

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Autores principales: Luo, Shanjun, Jiang, Xueqin, He, Yingbin, Li, Jianping, Jiao, Weihua, Zhang, Shengli, Xu, Fei, Han, Zhongcai, Sun, Jing, Yang, Jinpeng, Wang, Xiangyi, Ma, Xintian, Lin, Zeru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372391/
https://www.ncbi.nlm.nih.gov/pubmed/35968116
http://dx.doi.org/10.3389/fpls.2022.948249
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author Luo, Shanjun
Jiang, Xueqin
He, Yingbin
Li, Jianping
Jiao, Weihua
Zhang, Shengli
Xu, Fei
Han, Zhongcai
Sun, Jing
Yang, Jinpeng
Wang, Xiangyi
Ma, Xintian
Lin, Zeru
author_facet Luo, Shanjun
Jiang, Xueqin
He, Yingbin
Li, Jianping
Jiao, Weihua
Zhang, Shengli
Xu, Fei
Han, Zhongcai
Sun, Jing
Yang, Jinpeng
Wang, Xiangyi
Ma, Xintian
Lin, Zeru
author_sort Luo, Shanjun
collection PubMed
description Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R(2)) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m(2), 51.27 g/m(2), and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.
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spelling pubmed-93723912022-08-13 Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery Luo, Shanjun Jiang, Xueqin He, Yingbin Li, Jianping Jiao, Weihua Zhang, Shengli Xu, Fei Han, Zhongcai Sun, Jing Yang, Jinpeng Wang, Xiangyi Ma, Xintian Lin, Zeru Front Plant Sci Plant Science Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R(2)) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m(2), 51.27 g/m(2), and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9372391/ /pubmed/35968116 http://dx.doi.org/10.3389/fpls.2022.948249 Text en Copyright © 2022 Luo, Jiang, He, Li, Jiao, Zhang, Xu, Han, Sun, Yang, Wang, Ma and Lin. 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
Luo, Shanjun
Jiang, Xueqin
He, Yingbin
Li, Jianping
Jiao, Weihua
Zhang, Shengli
Xu, Fei
Han, Zhongcai
Sun, Jing
Yang, Jinpeng
Wang, Xiangyi
Ma, Xintian
Lin, Zeru
Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
title Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
title_full Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
title_fullStr Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
title_full_unstemmed Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
title_short Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
title_sort multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on uav multispectral imagery
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372391/
https://www.ncbi.nlm.nih.gov/pubmed/35968116
http://dx.doi.org/10.3389/fpls.2022.948249
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