🤖 AI Summary
This study addresses critical gaps in generative modeling for plant disease identification using deep learning (2018–2024), including limited application and inconsistent evaluation protocols. Based on 253 publications from the Scopus database, it conducts the first systematic bibliometric analysis, employing co-occurrence analysis, citation network mapping, keyword clustering, and quantitative assessment of four core performance metrics—accuracy, precision, recall, and F1-score. Results identify José M. C. de Toledo and Arnaldo R. Barbedo as central scholars, revealing strong international collaboration clusters. Generative models remain in an early exploratory phase: methodological diversity is high, yet standardized benchmarks are absent. Highly cited works predominantly focus on lightweight CNNs and data augmentation—not generative paradigms. The findings provide empirical foundations for establishing standardized evaluation frameworks, prioritizing algorithmic development directions, and refining research funding policies in this domain.
📝 Abstract
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.