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Evaluation of cultivated land quality using attention mechanism-back propagation neural network
Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated l...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044315/ https://www.ncbi.nlm.nih.gov/pubmed/35494807 http://dx.doi.org/10.7717/peerj-cs.948 |
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author | Liu, Yulin Li, Jiaolong Liu, Chuang Wei, Jiangshu |
author_facet | Liu, Yulin Li, Jiaolong Liu, Chuang Wei, Jiangshu |
author_sort | Liu, Yulin |
collection | PubMed |
description | Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated land quality, and there is little research on using deep learning for this purpose. Using Ya’an cultivated land as the research object, this study constructs an evaluation system for cultivated land quality based on seven aspects, including soil organic matter and soil texture. An attention mechanism (AM) is introduced into a back propagation (BP) neural network model. Therefore, an AM-BP neural network that is suitable for Ya’an cultivated land is designed. The sample is divided into training and test sets by a ratio of 7:3. We can output the evaluation results of cultivated land quality through experiments. Furthermore, they can be visualized through a pie chart. The experimental results indicate that the model effect of the AM-BP neural network is better than that of the BP neural network. That is, the mean square error is reduced by approximately 0.0019 and the determination coefficient is increased by approximately 0.005. In addition, this study obtains better results via the ensemble model. The quality of cultivated land in Yucheng District is generally good, i.e.,mostly third and fourth grades. It conforms to the normal distribution. Lastly, the method has certain to evaluate cultivated land quality, providing a reference for future cultivated land quality evaluation. |
format | Online Article Text |
id | pubmed-9044315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90443152022-04-28 Evaluation of cultivated land quality using attention mechanism-back propagation neural network Liu, Yulin Li, Jiaolong Liu, Chuang Wei, Jiangshu PeerJ Comput Sci Algorithms and Analysis of Algorithms Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated land quality, and there is little research on using deep learning for this purpose. Using Ya’an cultivated land as the research object, this study constructs an evaluation system for cultivated land quality based on seven aspects, including soil organic matter and soil texture. An attention mechanism (AM) is introduced into a back propagation (BP) neural network model. Therefore, an AM-BP neural network that is suitable for Ya’an cultivated land is designed. The sample is divided into training and test sets by a ratio of 7:3. We can output the evaluation results of cultivated land quality through experiments. Furthermore, they can be visualized through a pie chart. The experimental results indicate that the model effect of the AM-BP neural network is better than that of the BP neural network. That is, the mean square error is reduced by approximately 0.0019 and the determination coefficient is increased by approximately 0.005. In addition, this study obtains better results via the ensemble model. The quality of cultivated land in Yucheng District is generally good, i.e.,mostly third and fourth grades. It conforms to the normal distribution. Lastly, the method has certain to evaluate cultivated land quality, providing a reference for future cultivated land quality evaluation. PeerJ Inc. 2022-04-11 /pmc/articles/PMC9044315/ /pubmed/35494807 http://dx.doi.org/10.7717/peerj-cs.948 Text en ©2022 Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Liu, Yulin Li, Jiaolong Liu, Chuang Wei, Jiangshu Evaluation of cultivated land quality using attention mechanism-back propagation neural network |
title | Evaluation of cultivated land quality using attention mechanism-back propagation neural network |
title_full | Evaluation of cultivated land quality using attention mechanism-back propagation neural network |
title_fullStr | Evaluation of cultivated land quality using attention mechanism-back propagation neural network |
title_full_unstemmed | Evaluation of cultivated land quality using attention mechanism-back propagation neural network |
title_short | Evaluation of cultivated land quality using attention mechanism-back propagation neural network |
title_sort | evaluation of cultivated land quality using attention mechanism-back propagation neural network |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044315/ https://www.ncbi.nlm.nih.gov/pubmed/35494807 http://dx.doi.org/10.7717/peerj-cs.948 |
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