Navigating the Landscape of Artificial Intelligence in Agricultural Extension Services: A Bibliometric Analysis
محورهای موضوعی : Agricultural Extension
Gabriel Shimasaan Iorundu
1
,
Charity Ojochogwu Egbunu
2
1 - Department of Computer Sciences, Joseph Sarwuan Tarka University, Makurdi, Nigeria
2 - Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia
کلید واژه: Artificial Intelligence, Agricultural Extension, Machine Learning, Smart Farming,
چکیده مقاله :
Artificial Intelligence (AI) is increasingly adopted in agricultural extension services to enhance knowledge sharing, improve decision-making, and promote sustainable farming methods. This study presents a bibliometric analysis of the global research landscape on the application of AI in agricultural extension. Using data from two major sources, Web of Science and Scopus databases, we analyzed publication trends, co-authorship network, bibliographic coupling, and co-occurrence network from 1997 to 2024. The findings reveal a slow but steady rise in publications. Indonesia, the United States of America, India, China, and the United Kingdom emerged as the top five countries in publication count, with notable contributions from developed and underdeveloped countries. The co-authorship and bibliographic coupling networks represent a high level of collaboration among the participating countries with global access to research on AI in agricultural extension. Keyword analysis highlights a strong emphasis on technological innovation in AI-driven agricultural extension, with an emerging focus on areas such as machine learning, farmers’ knowledge, adoption, agricultural practices, and climate-smart agriculture. We recommend that stakeholders in the agricultural sector invest in the development of localized and context-aware AI applications. In addition, strengthen capacity-building efforts to ensure widespread and equitable adoption of these technologies in rural advisory services.
Artificial Intelligence (AI) is increasingly adopted in agricultural extension services to enhance knowledge sharing, improve decision-making, and promote sustainable farming methods. This study presents a bibliometric analysis of the global research landscape on the application of AI in agricultural extension. Using data from two major sources, Web of Science and Scopus databases, we analyzed publication trends, co-authorship network, bibliographic coupling, and co-occurrence network from 1997 to 2024. The findings reveal a slow but steady rise in publications. Indonesia, the United States of America, India, China, and the United Kingdom emerged as the top five countries in publication count, with notable contributions from developed and underdeveloped countries. The co-authorship and bibliographic coupling networks represent a high level of collaboration among the participating countries with global access to research on AI in agricultural extension. Keyword analysis highlights a strong emphasis on technological innovation in AI-driven agricultural extension, with an emerging focus on areas such as machine learning, farmers’ knowledge, adoption, agricultural practices, and climate-smart agriculture. We recommend that stakeholders in the agricultural sector invest in the development of localized and context-aware AI applications. In addition, strengthen capacity-building efforts to ensure widespread and equitable adoption of these technologies in rural advisory services.
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Navigating the Landscape of Artificial Intelligence in Agricultural Extension Services: A Bibliometric Analysis
Gabriel Shimasaan Iorundu1* and Charity Ojochogwu Egbunu2
1,2Department of Computer Sciences, Joseph Sarwuan Tarka University, Makurdi, Nigeria
2Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia
*Corresponding Author Email: iorundu.gabriel@uam.edu.ng
A
Keywords Artificial Intelligence, Agricultural Extension, Machine Learning, Smart Farming |
Abstract |
