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Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. T...

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Detalles Bibliográficos
Autores principales: Bhagat, Priya Rani, Naz, Farheen, Magda, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182339/
https://www.ncbi.nlm.nih.gov/pubmed/35679287
http://dx.doi.org/10.1371/journal.pone.0268989
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author Bhagat, Priya Rani
Naz, Farheen
Magda, Robert
author_facet Bhagat, Priya Rani
Naz, Farheen
Magda, Robert
author_sort Bhagat, Priya Rani
collection PubMed
description There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.
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spelling pubmed-91823392022-06-10 Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis Bhagat, Priya Rani Naz, Farheen Magda, Robert PLoS One Research Article There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities. Public Library of Science 2022-06-09 /pmc/articles/PMC9182339/ /pubmed/35679287 http://dx.doi.org/10.1371/journal.pone.0268989 Text en © 2022 Bhagat 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bhagat, Priya Rani
Naz, Farheen
Magda, Robert
Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
title Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
title_full Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
title_fullStr Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
title_full_unstemmed Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
title_short Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
title_sort artificial intelligence solutions enabling sustainable agriculture: a bibliometric analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182339/
https://www.ncbi.nlm.nih.gov/pubmed/35679287
http://dx.doi.org/10.1371/journal.pone.0268989
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