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Advances in deep learning have enabled effective applications in agriculture, including fruit disease detection. Accurate identification of diseases in fruits such as Annonaceae and Rutaceae families is crucial for yield and quality improvements. Many studies employ deep learning models like CNN, ResNet, VGG, and DenseNet for disease detection across fruits such as apples, oranges, guavas, and grapes. This article reviews recent research on deep learning for fruit disease detection and classification, focusing on model performance, data utilization, and visualization techniques. We analyze existing studies to identify optimal strategies for fruit species and other underrepresented crops, outlining challenges and areas for future research on various types of fruit species and their family.

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Survey of Deep Learning Models for Disease Detection in Various Fruit Species

  • 会议论文

  • 发表日期：

    2026年

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  • Ashutosh Patil,
  • Gayatri Bhangle,
  • Sejal Kadam,
  • Poonam Vetal,
  • Prajakta Shinde

摘要

Advances in deep learning have enabled effective applications in agriculture, including fruit disease detection. Accurate identification of diseases in fruits such as Annonaceae and Rutaceae families is crucial for yield and quality improvements. Many studies employ deep learning models like CNN, ResNet, VGG, and DenseNet for disease detection across fruits such as apples, oranges, guavas, and grapes. This article reviews recent research on deep learning for fruit disease detection and classification, focusing on model performance, data utilization, and visualization techniques. We analyze existing studies to identify optimal strategies for fruit species and other underrepresented crops, outlining challenges and areas for future research on various types of fruit species and their family.