Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions
<div><p>As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologi...
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2023
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| _version_ | 1864513526449569792 |
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| author | Arslan Yousaf (18021805) |
| author2 | Vahid Kayvanfar (17876921) Annamaria Mazzoni (13751504) Adel Elomri (8984063) |
| author2_role | author author author |
| author_facet | Arslan Yousaf (18021805) Vahid Kayvanfar (17876921) Annamaria Mazzoni (13751504) Adel Elomri (8984063) |
| author_role | author |
| dc.creator.none.fl_str_mv | Arslan Yousaf (18021805) Vahid Kayvanfar (17876921) Annamaria Mazzoni (13751504) Adel Elomri (8984063) |
| dc.date.none.fl_str_mv | 2023-01-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fsufs.2022.1053921 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Artificial_intelligence-based_decision_support_systems_in_smart_agriculture_Bibliometric_analysis_for_operational_insights_and_future_directions/25285090 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Food sciences Biological sciences Ecology smart agriculture precision agriculture Agriculture 4.0 Internet of Things artificial intelligence machine learning bibliometric analysis operations research |
| dc.title.none.fl_str_mv | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Sustainable Food Systems<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fsufs.2022.1053921" target="_blank">https://dx.doi.org/10.3389/fsufs.2022.1053921</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e10380fe93be5ce3704f7206315c0925 |
| identifier_str_mv | 10.3389/fsufs.2022.1053921 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25285090 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directionsArslan Yousaf (18021805)Vahid Kayvanfar (17876921)Annamaria Mazzoni (13751504)Adel Elomri (8984063)Agricultural, veterinary and food sciencesFood sciencesBiological sciencesEcologysmart agricultureprecision agricultureAgriculture 4.0Internet of Thingsartificial intelligencemachine learningbibliometric analysisoperations research<div><p>As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Sustainable Food Systems<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fsufs.2022.1053921" target="_blank">https://dx.doi.org/10.3389/fsufs.2022.1053921</a></p>2023-01-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fsufs.2022.1053921https://figshare.com/articles/journal_contribution/Artificial_intelligence-based_decision_support_systems_in_smart_agriculture_Bibliometric_analysis_for_operational_insights_and_future_directions/25285090CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252850902023-01-09T03:00:00Z |
| spellingShingle | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions Arslan Yousaf (18021805) Agricultural, veterinary and food sciences Food sciences Biological sciences Ecology smart agriculture precision agriculture Agriculture 4.0 Internet of Things artificial intelligence machine learning bibliometric analysis operations research |
| status_str | publishedVersion |
| title | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| title_full | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| title_fullStr | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| title_full_unstemmed | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| title_short | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| title_sort | Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions |
| topic | Agricultural, veterinary and food sciences Food sciences Biological sciences Ecology smart agriculture precision agriculture Agriculture 4.0 Internet of Things artificial intelligence machine learning bibliometric analysis operations research |