分析2018-2024年植物病害识别深度学习研究，揭示关键作者与技术趋势。
Leveraging deep learning for plant disease identification: a bibliometric analysis in SCOPUS from 2018 to 2024
- 基于SCOPUS253篇文献的计量分析，聚焦生成建模方法
- 太与阿纳尔·巴尔贝多研究被引量高，影响显著
- 发现合作网络与新兴研究空白，助力未来方向制定
本研究对2018至2024年间基于深度学习的植物病害识别研究进行文献计量分析，重点关注生成建模。基于SCOPUS数据库的253篇文献数据，系统分析了准确率、精确率、召回率与F1分数等关键性能指标。结果表明，太（Too）与阿纳尔·巴尔贝多（Arnal Barbedo）的研究具有显著的被引次数，显示其在学术界的影响力。共著者网络揭示了紧密的合作集群，关键词分析则识别出若干新兴研究空白。研究强调合作与引用指标在塑造研究方向和提升学术影响力方面的重要作用。未来应深入探讨高被引研究的方法论，以指导最佳实践与政策制定。
原文摘要 · Abstract (English)
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.
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