Bibliometric analysis of deep learning in plant disease management

Abstract

Deep learning has gained significant importance in manifold disciplines such as natural language processing, supply chain optimization, computer vision, financial analysis, mechatronics and robotics, cybersecurity, and healthcare. It offers alternative methods to proactively manage plant diseases to ensure healthy crop yields, minimize economic losses, contribute to global food security, and promote sustainable agricultural practices. Nevertheless, despite a huge volume of publications on plant disease management using deep learning, a gap exists in the methodical evaluation of the contributions, impacts, trends, and exploration of intellectual structures of the publication elements using bibliometric analysis. Therefore, a bibliometric analysis was performed on 4,317 publications indexed in the Scopus database from 2016 to 2025 regarding plant disease management utilizing deep learning methods. Bibliometric performance analysis was based on publication, citation, and citation-and-publication metrics. Science mapping was conducted based on citation analysis, co-authorship analysis, bibliographic coupling, and co-word analysis using Biblioshiny and VOSviewer tools. The bibliometric analysis confirmed that Computers and Electronics in Agriculture and IEEE Access are the most impactful publication sources according to the metrics of h-index and citations. A publication written by Mohanty SP in 2016 was found to be the most globally cited. Five distinctive clusters were identified using bibliographic coupling of publications and co-word analysis of author keywords to provide useful insights into the knowledge structure of plant disease management using deep learning. The analysis findings can provide valuable insights into the broader impact of the extant literature on deep learning applications, offering a footing for progressing artificial intelligence applications in plant disease management and guiding future research directions.