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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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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. |
format | Online Article Text |
id | pubmed-9357766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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