Leveraging deep learning for plant disease identification: a bibliometric analysis in SCOPUS from 2018 to 2024
This is an incremental study that provides insights for researchers and policymakers in agricultural AI by mapping the academic landscape of plant disease identification.
This paper conducted a bibliometric analysis of 253 documents from 2018 to 2024 to examine deep learning research for plant disease identification, focusing on generative modeling and analyzing metrics like accuracy and F1-score, while identifying key authors and collaborative trends.
This work aimed to present a bibliometric analysis of deep learning research for plant disease identification, with a special focus on generative modeling. A thorough analysis of SCOPUS-sourced bibliometric data from 253 documents was performed. Key performance metrics such as accuracy, precision, recall, and F1-score were analyzed for generative modeling. The findings highlighted significant contributions from some authors Too and Arnal Barbedo, whose works had notable citation counts, suggesting their influence on the academic community. Co-authorship networks revealed strong collaborative clusters, while keyword analysis identified emerging research gaps. This study highlights the role of collaboration and citation metrics in shaping research directions and enhancing the impact of scholarly work in applications of deep learning to plant disease identification. Future research should explore the methodologies of highly cited studies to inform best practices and policy-making.