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<p>Rice disease detection is vital for food security, prevention efficiency, pesticide reduction, and sustainable agriculture. Challenges like poor model applicability, low accuracy, and limited datasets make this research essential. Existing models face issues with large parameters, complex computations, and insufficient semantic information capture. This paper introduces a large rice disease dataset and proposes the RiceDetect-Net model to address these challenges. The model integrates a brand-new lightweight detection head LE-Head to reduce the parameter quantity and computational complexity. To boost accuracy, the model integrates the newest FCA attention mechanism into its high-level semantic processing component, strengthening its capacity to interpret complex semantic data. Testing on a custom rice disease dataset comprising 54,240 images, the model attained 94.3% accuracy with a parameter count of 2.32&#xa0;M. The enhanced model achieves a 0.4% increase in accuracy while reducing parameters by 10% relative to the baseline YOLOv11. The detection model is more lightweight, can adapt to the computing power of field detection equipment, is more suitable for practical scenario applications, and provides technical support for the development of smart agriculture.</p>

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RiceDetect-Net: a lightweight real-time detection framework for rice diseases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

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  • Xinhui Yuan,
  • Qin Xu,
  • Tao Wang,
  • Lu Gao,
  • Guangwu Zhao,
  • Liangquan Jia,
  • Yang Wang

摘要

Rice disease detection is vital for food security, prevention efficiency, pesticide reduction, and sustainable agriculture. Challenges like poor model applicability, low accuracy, and limited datasets make this research essential. Existing models face issues with large parameters, complex computations, and insufficient semantic information capture. This paper introduces a large rice disease dataset and proposes the RiceDetect-Net model to address these challenges. The model integrates a brand-new lightweight detection head LE-Head to reduce the parameter quantity and computational complexity. To boost accuracy, the model integrates the newest FCA attention mechanism into its high-level semantic processing component, strengthening its capacity to interpret complex semantic data. Testing on a custom rice disease dataset comprising 54,240 images, the model attained 94.3% accuracy with a parameter count of 2.32 M. The enhanced model achieves a 0.4% increase in accuracy while reducing parameters by 10% relative to the baseline YOLOv11. The detection model is more lightweight, can adapt to the computing power of field detection equipment, is more suitable for practical scenario applications, and provides technical support for the development of smart agriculture.