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Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network

Rice developing prognostication is a key part of precise agricultural management, and its advancement is an intricate course of events involving the interplay of breed and environmental element. The traditional research method is based on data analysis of rice growth prediction modeling, mining the...

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Detalles Bibliográficos
Autores principales: Jiao, Feng, Chen, Yang, Zhang, Xinyue, Zhou, Yuyue, Wang, Linlin, Wu, Jinhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357766/
https://www.ncbi.nlm.nih.gov/pubmed/35958786
http://dx.doi.org/10.1155/2022/2151682
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author Jiao, Feng
Chen, Yang
Zhang, Xinyue
Zhou, Yuyue
Wang, Linlin
Wu, Jinhua
author_facet Jiao, Feng
Chen, Yang
Zhang, Xinyue
Zhou, Yuyue
Wang, Linlin
Wu, Jinhua
author_sort Jiao, Feng
collection PubMed
description Rice developing prognostication is a key part of precise agricultural management, and its advancement is an intricate course of events involving the interplay of breed and environmental element. The traditional research method is based on data analysis of rice growth prediction modeling, mining the concealed rapport between rice productivity and circumstance element, for instance, weather, sunlight, and water, and then predicting its yield and analyzing the complex rapport between the circumstance element and growth in every developing phase. In this dissertation, the improved ElmanNN is accustomed to establish a prediction model, and the ElmanNN is accustomed to determine the rapport between the circumstance element and growth in every developing phase simultaneously so as to avoid the arithmetic falling into local optimum easily. In this dissertation, the improved genetic arithmetic is accustomed to optimize the initial weight and threshold of Elman neural network, and the range of weight value multitudinous layers in the mould are obtained by training the network with samples that have been tested in the last few years. Finally, the rapport between growth and yield in six different periods is independently modeled, and the training samples are build up separately one by one based on physiological parameters and environmental indicators of rice at every level. The experiments show that the accuracy for the prediction model in the light of the improved ElmanNN has been beneficial.
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spelling pubmed-93577662022-08-10 Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network Jiao, Feng Chen, Yang Zhang, Xinyue Zhou, Yuyue Wang, Linlin Wu, Jinhua Comput Intell Neurosci Research Article Rice developing prognostication is a key part of precise agricultural management, and its advancement is an intricate course of events involving the interplay of breed and environmental element. The traditional research method is based on data analysis of rice growth prediction modeling, mining the concealed rapport between rice productivity and circumstance element, for instance, weather, sunlight, and water, and then predicting its yield and analyzing the complex rapport between the circumstance element and growth in every developing phase. In this dissertation, the improved ElmanNN is accustomed to establish a prediction model, and the ElmanNN is accustomed to determine the rapport between the circumstance element and growth in every developing phase simultaneously so as to avoid the arithmetic falling into local optimum easily. In this dissertation, the improved genetic arithmetic is accustomed to optimize the initial weight and threshold of Elman neural network, and the range of weight value multitudinous layers in the mould are obtained by training the network with samples that have been tested in the last few years. Finally, the rapport between growth and yield in six different periods is independently modeled, and the training samples are build up separately one by one based on physiological parameters and environmental indicators of rice at every level. The experiments show that the accuracy for the prediction model in the light of the improved ElmanNN has been beneficial. Hindawi 2022-07-31 /pmc/articles/PMC9357766/ /pubmed/35958786 http://dx.doi.org/10.1155/2022/2151682 Text en Copyright © 2022 Feng Jiao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jiao, Feng
Chen, Yang
Zhang, Xinyue
Zhou, Yuyue
Wang, Linlin
Wu, Jinhua
Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
title Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
title_full Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
title_fullStr Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
title_full_unstemmed Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
title_short Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
title_sort prediction model of rice seedling growth and rhizosphere fertility based on the improved elman neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357766/
https://www.ncbi.nlm.nih.gov/pubmed/35958786
http://dx.doi.org/10.1155/2022/2151682
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