Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wu, Juai, Zhao, Fangzhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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
_version_ 1785025727873155072
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
work_keys_str_mv AT wujuai machinelearninganeffectivetechnicalmethodforfutureuseinassessingtheeffectivenessofphosphorusdissolvingmicrobialagroremediation
AT zhaofangzhou machinelearninganeffectivetechnicalmethodforfutureuseinassessingtheeffectivenessofphosphorusdissolvingmicrobialagroremediation