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...
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| Published in | Frontiers in bioengineering and biotechnology Vol. 11; p. 1189166 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Frontiers Media S.A
31.03.2023
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Zhao, Fangzhou Wu, Juai |
| AuthorAffiliation | 1 College of Automation & College of Artificial Intelligence , Nanjing University of Posts and Telecommunications , Nanjing , China 2 School of Environmental and Biological Engineering , Nanjing University of Science and Technology , Nanjing , China |
| AuthorAffiliation_xml | – name: 1 College of Automation & College of Artificial Intelligence , Nanjing University of Posts and Telecommunications , Nanjing , China – name: 2 School of Environmental and Biological Engineering , Nanjing University of Science and Technology , Nanjing , China |
| Author_xml | – sequence: 1 givenname: Juai surname: Wu fullname: Wu, Juai – sequence: 2 givenname: Fangzhou surname: Zhao fullname: Zhao, Fangzhou |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37064244$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s13205-014-0206-0 10.1109/tkde.2019.2915231 10.3390/agronomy12051003 10.1038/nature14539 10.1016/j.compenvurbsys.2022.101789 10.1016/j.scitotenv.2022.157850 10.1016/j.compbiomed.2021.104672 10.3390/agronomy12071639 10.1016/j.eng.2021.03.019 10.1016/j.envpol.2020.116281 10.1016/j.ecoenv.2014.07.001 10.1109/tits.2015.2466695 10.1016/j.ecoenv.2017.06.051 10.1016/j.envint.2019.03.068 10.1016/j.jhazmat.2023.131176 10.1016/j.jclepro.2022.130942 10.1016/j.procs.2023.01.241 10.1016/j.scitotenv.2020.140338 10.1016/j.chemosphere.2022.137623 10.1016/j.scitotenv.2020.142570 10.1016/j.geodrs.2022.e00569 10.1007/s42729-020-00342-7 10.1016/j.procs.2023.01.023 10.1016/j.ecolind.2021.107608 10.1016/j.aiig.2022.12.003 10.1016/j.chemosphere.2020.128626 10.1016/j.enmf.2022.07.005 10.1016/j.catena.2022.106798 10.1007/s11738-016-2133-7 10.3389/fbioe.2023.1127166 10.1016/j.envpol.2022.119248 10.3389/fmicb.2017.00971 10.1016/j.advengsoft.2022.103326 |
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| Keywords | microbial remediation agricultural pollution machine learning assessment and prediction crop yield |
| Language | English |
| License | Copyright © 2023 Wu and Zhao. 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. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Da Tian, Anhui Agricultural University, China Pei Peng, Rutgers, The State University of New Jersey, United States Reviewed by: Mengying Zhang, Shanghai Institute of Microsystem and Information Technology (CAS), China Zhaoxia Duan, Hohai University, China This article was submitted to Bioprocess Engineering, a section of the journal Frontiers in Bioengineering and Biotechnology |
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| Title | Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation |
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