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Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation
The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102617/ https://www.ncbi.nlm.nih.gov/pubmed/37064244 http://dx.doi.org/10.3389/fbioe.2023.1189166 |
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author | Wu, Juai Zhao, Fangzhou |
author_facet | Wu, Juai Zhao, Fangzhou |
author_sort | Wu, Juai |
collection | PubMed |
description | The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has been a difficult issue. Machine learning (ML), a widely used data processing technique, can improve the accuracy of assessments and predictions by analyzing and processing large amounts of data. In microbial remediation, ML can help identify the types of microbes, mechanisms of action and adapted environments, predict the effectiveness of microbial remediation and potential problems, and assess the ecological benefits and crop growth after remediation. In addition, ML can help optimize monitoring programs, improve the accuracy and effectiveness of heavy metal pollution monitoring, and provide a scientific basis for the development of treatment measures. Therefore, ML has important application prospects in assessing the effectiveness of microbial remediation of heavy metal pollution in agriculture and is expected to be an effective pollution management technology. |
format | Online Article Text |
id | pubmed-10102617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101026172023-04-15 Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation Wu, Juai Zhao, Fangzhou Front Bioeng Biotechnol Bioengineering and Biotechnology The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has been a difficult issue. Machine learning (ML), a widely used data processing technique, can improve the accuracy of assessments and predictions by analyzing and processing large amounts of data. In microbial remediation, ML can help identify the types of microbes, mechanisms of action and adapted environments, predict the effectiveness of microbial remediation and potential problems, and assess the ecological benefits and crop growth after remediation. In addition, ML can help optimize monitoring programs, improve the accuracy and effectiveness of heavy metal pollution monitoring, and provide a scientific basis for the development of treatment measures. Therefore, ML has important application prospects in assessing the effectiveness of microbial remediation of heavy metal pollution in agriculture and is expected to be an effective pollution management technology. Frontiers Media S.A. 2023-03-31 /pmc/articles/PMC10102617/ /pubmed/37064244 http://dx.doi.org/10.3389/fbioe.2023.1189166 Text en Copyright © 2023 Wu and Zhao. 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 | Bioengineering and Biotechnology Wu, Juai Zhao, Fangzhou Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
title | Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
title_full | Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
title_fullStr | Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
title_full_unstemmed | Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
title_short | Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
title_sort | machine learning: an effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102617/ https://www.ncbi.nlm.nih.gov/pubmed/37064244 http://dx.doi.org/10.3389/fbioe.2023.1189166 |
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