Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

Abstract Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and...

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Autores principales:	Xueqin Jiang, Shanjun Luo, Shenghui Fang, Bowen Cai, Qiang Xiong, Yanyan Wang, Xia Huang, Xiaojuan Liu
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Total iron content Harmonic analysis Wavelet packet transform Principal component analysis BP neural network Plant culture SB1-1110 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1019cbfdcfa64c05864bc2e32fef0082
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spelling	oai:doaj.org-article:1019cbfdcfa64c05864bc2e32fef00822021-11-14T12:11:14ZRemotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network10.1186/s13007-021-00812-81746-4811https://doaj.org/article/1019cbfdcfa64c05864bc2e32fef00822021-11-01T00:00:00Zhttps://doi.org/10.1186/s13007-021-00812-8https://doaj.org/toc/1746-4811Abstract Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.Xueqin JiangShanjun LuoShenghui FangBowen CaiQiang XiongYanyan WangXia HuangXiaojuan LiuBMCarticleTotal iron contentHarmonic analysisWavelet packet transformPrincipal component analysisBP neural networkPlant cultureSB1-1110Biology (General)QH301-705.5ENPlant Methods, Vol 17, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Total iron content Harmonic analysis Wavelet packet transform Principal component analysis BP neural network Plant culture SB1-1110 Biology (General) QH301-705.5
spellingShingle	Total iron content Harmonic analysis Wavelet packet transform Principal component analysis BP neural network Plant culture SB1-1110 Biology (General) QH301-705.5 Xueqin Jiang Shanjun Luo Shenghui Fang Bowen Cai Qiang Xiong Yanyan Wang Xia Huang Xiaojuan Liu Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network
description	Abstract Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.
format	article
author	Xueqin Jiang Shanjun Luo Shenghui Fang Bowen Cai Qiang Xiong Yanyan Wang Xia Huang Xiaojuan Liu
author_facet	Xueqin Jiang Shanjun Luo Shenghui Fang Bowen Cai Qiang Xiong Yanyan Wang Xia Huang Xiaojuan Liu
author_sort	Xueqin Jiang
title	Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network
title_short	Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network
title_full	Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network
title_fullStr	Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network
title_full_unstemmed	Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network
title_sort	remotely sensed estimation of total iron content in soil with harmonic analysis and bp neural network
publisher	BMC
publishDate	2021
url	https://doaj.org/article/1019cbfdcfa64c05864bc2e32fef0082
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Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

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