Artificial intelligence for crop disease detection and management: A bibliometric analysis of global research trends

Abstract

Artificial intelligence (AI) has emerged as a transformative technology for crop disease detection and management by enabling rapid, accurate and automated disease diagnosis to support sustainable agricultural production. This study presents a bibliometric analysis of global research trends in AI-based crop disease detection and management from 2016 to 2026. Bibliographic data were retrieved from the Scopus database using a structured search strategy. A total of 3729 records were initially identified and 605 publications were retained after applying predefined inclusion and exclusion criteria. Bibliometric indicators and VOSviewer software were employed to analyse annual publication trends, leading contributors, collaboration networks, keyword co-occurrence patterns, bibliographic coupling relationships and co-citation structures. The results reveal a substantial increase in research output, particularly after 2021, indicating growing scientific interest in AI-enabled crop protection technologies. Keyword analysis identified deep learning, machine learning, computer vision, transfer learning and plant disease detection as the dominant research themes. In contrast, co-authorship and co-citation analyses revealed strong international collaboration and a well-established intellectual foundation.