Scientometric Analysis of Scientific Outputs of Artificial Intelligence in Agriculture Subject Area: Analysis of Trends, Collaboration Networks, and Conceptual Structure(2021-2025)

Document Type : Extension

Authors

1 Department of Knowledge and Information Science, Faculty of Psychology and Educational Sciences, University of Isfahan, Isfahan, Iran

2 Department of Knowledge and Information Science, Faculty of Psychology and Educational Sciences, Yazd University, Yazd, Iran

Abstract

Objective: This study was conducted in the field of artificial intelligence in agriculture during the years 2021 to 2025.

Methodology: This research is applied in terms of purpose and descriptive-scientometric. The statistical population includes 12,230 articles indexed in the Web.AV.Science citation database. Data were analyzed using Excel, VOSViewer, RStudio.

Findings: The journals Smart Agricultural Technology, Agriculture-Basel, and Agronomy-Basel were the most productive sources. The most cited articles focused on the themes of “climate change adaptation,” “blockchain in supply chains,” and “applications of artificial intelligence.” Keyword analysis identified “machine learning”, “deep learning”, and “precision agriculture” as central concepts. Five main thematic clusters were extracted: yield modeling and prediction, remote sensing and vegetation monitoring, climate change and risk management, smart aquaculture, and environmental pollution.

Results: The field of AI in agriculture is in the “rapid growth” stage of the scientific life cycle, and the focus of knowledge is on the convergence of machine learning technologies with operational challenges such as climate change and resource management. Clusters related to “climate change” and “crop performance” are in a central and mature position in the knowledge network, while topics related to “classification algorithms” are of an emerging nature and have high potential for future research.

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Main Subjects

Journal of Agricultural Information Sciences and Technology: Agricultural Intelligence
Volume 9, Issue 2 - Serial Number 18
September 2026
Pages 45-56

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History

  • Receive Date: 18 March 2026
  • Revise Date: 29 June 2026
  • Accept Date: 22 June 2026

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