Scholarlink

Objective <p>Minimizing crop losses through the early detection of plant diseases is vital for enhancing global agricultural efficiency. While deep learning has emerged as a promising solution, a significant gap exists between laboratory performance and practical, in-field utility. This study evaluates this discrepancy through a dual-methodological approach.</p> Methods <p>First, a tertiary systematic literature review was conducted, synthesizing 22 secondary reviews encompassing over 750 unique primary studies to establish the current state of the art. Second, an empirical validation was performed using a VGG16 transfer learning model trained on three distinct dataset types, which vary in scale (small vs. large), environment (laboratory vs. in-field), and condition (raw vs. pre-processed).</p> Results <p>The tertiary review identifies Convolutional Neural Networks, particularly VGG architectures, as the leading model but highlights a critical reliance on private and unrealistic datasets. Furthermore, the analysis reveals that Accuracy, the most common metric, is often insufficient for evaluating the imbalanced datasets typical of the field. Empirical results corroborate these findings, demonstrating that VGG16 performance is highly dependent on dataset characteristics; models perform significantly better on large, pre-processed laboratory data than on realistic in-field datasets.&#xa0;</p> Conclusion <p> These findings suggest that many current models remain inapplicable to real-world agricultural scenarios. To bridge this reality gap, future research must prioritize the development of open-source, standardized, and validated in-field datasets to ensure the reliability and scalability of automated disease detection systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A tertiary systematic literature review and experimental evaluation of deep learning models for plant disease detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

查看全文
  • Kwabena Ebo Bennin,
  • Dide van Teeffelen,
  • William Hurst,
  • Önder Babur

摘要

Objective

Minimizing crop losses through the early detection of plant diseases is vital for enhancing global agricultural efficiency. While deep learning has emerged as a promising solution, a significant gap exists between laboratory performance and practical, in-field utility. This study evaluates this discrepancy through a dual-methodological approach.

Methods

First, a tertiary systematic literature review was conducted, synthesizing 22 secondary reviews encompassing over 750 unique primary studies to establish the current state of the art. Second, an empirical validation was performed using a VGG16 transfer learning model trained on three distinct dataset types, which vary in scale (small vs. large), environment (laboratory vs. in-field), and condition (raw vs. pre-processed).

Results

The tertiary review identifies Convolutional Neural Networks, particularly VGG architectures, as the leading model but highlights a critical reliance on private and unrealistic datasets. Furthermore, the analysis reveals that Accuracy, the most common metric, is often insufficient for evaluating the imbalanced datasets typical of the field. Empirical results corroborate these findings, demonstrating that VGG16 performance is highly dependent on dataset characteristics; models perform significantly better on large, pre-processed laboratory data than on realistic in-field datasets. 

Conclusion

These findings suggest that many current models remain inapplicable to real-world agricultural scenarios. To bridge this reality gap, future research must prioritize the development of open-source, standardized, and validated in-field datasets to ensure the reliability and scalability of automated disease detection systems.