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<p>Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalability, frequently necessitating specialized equipment and expertise. This study presents a deep learning-based automated diagnostic system for rice leaf diseases, leveraging a large-scale dataset comprising annotated images spanning six common rice diseases: bacterial stripe, false smut, leaf blast, neck blast, sheath blight, and brown spot. We evaluated seven advanced deep learning architectures—MobileNetV2, GoogLeNet, EfficientNet, ResNet-34, DenseNet-121, VGG16, and ShuffleNetV2—across a range of performance metrics including precision, recall, and overall diagnostic accuracy. Among these, GoogLeNet, DenseNet-121, ResNet-34, and VGG16 demonstrated superior performance, particularly in minimizing class confusion and enhancing diagnostic accuracy. These models were selected based on diverse architectural principles to ensure complementary feature extraction capabilities. An ensemble model, integrating these four high-performing networks via a simple average fusion strategy, was subsequently developed, significantly reducing misclassification rates and providing robust, scalable diagnostic capabilities suitable for deployment in real-world agricultural settings. The model’s performance was further validated on independent test data collected under varying environmental conditions.</p>

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Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Prameetha Pai,
  • S. Amutha,
  • Seema Patil,
  • T. Shobha,
  • Mustafa Basthikodi,
  • B. M. Ahamed Shafeeq,
  • Ananth Prabhu Gurpur

摘要

Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalability, frequently necessitating specialized equipment and expertise. This study presents a deep learning-based automated diagnostic system for rice leaf diseases, leveraging a large-scale dataset comprising annotated images spanning six common rice diseases: bacterial stripe, false smut, leaf blast, neck blast, sheath blight, and brown spot. We evaluated seven advanced deep learning architectures—MobileNetV2, GoogLeNet, EfficientNet, ResNet-34, DenseNet-121, VGG16, and ShuffleNetV2—across a range of performance metrics including precision, recall, and overall diagnostic accuracy. Among these, GoogLeNet, DenseNet-121, ResNet-34, and VGG16 demonstrated superior performance, particularly in minimizing class confusion and enhancing diagnostic accuracy. These models were selected based on diverse architectural principles to ensure complementary feature extraction capabilities. An ensemble model, integrating these four high-performing networks via a simple average fusion strategy, was subsequently developed, significantly reducing misclassification rates and providing robust, scalable diagnostic capabilities suitable for deployment in real-world agricultural settings. The model’s performance was further validated on independent test data collected under varying environmental conditions.