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<p>In rice cultivation, manually identifying rice blast disease is time-consuming and labor-intensive. Computer vision technology enables real-time and accurate disease detection in complex rice field environments. However, conventional object detection models face challenges in direct deployment on embedded edge devices due to their high computational demands. To address this issue, this paper proposes a lightweight rice blast detection model based on YOLOv8n. First, a Star Operation is introduced to optimize the C2f module in YOLOv8, enhancing the model’s ability to extract multi-scale features of rice blast disease. Second, a lightweight High-level Screening Feature Pyramid Network (HSFPN) is adopted to improve feature fusion efficiency, and a detection head incorporating shared parameters and Detail Enhancement Convolution (DEConv) is designed to reduce computational costs while improving detection accuracy. Finally, the LAMP pruning algorithm is applied to remove redundant parameters, further lightweighting the model. Experimental results demonstrate that, compared to YOLOv8n (3.01M parameters, 8.1 GFLOPs), the proposed model reduces parameters by 88% (from 3.01M to 0.36M) and decreases computational cost by 59.2% (from 8.1 GFLOPs to 3.3 GFLOPs), while maintaining an mAP@50 of 78.6% (an improvement of 0.2%). Furthermore, the model achieves a real-time performance of 22.5 FPS on the Jetson Nano, providing practical value for rice blast disease detection and rice crop protection.</p>

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Lightweight YOLOv8-based real-time detection of rice blast for edge computing devices

  • 期刊论文

  • 发表日期：

    2025年4月18日

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  • Song Yang,
  • Xiaoyun Ma,
  • Dayang Liu,
  • Liangkuan Zhu

摘要

In rice cultivation, manually identifying rice blast disease is time-consuming and labor-intensive. Computer vision technology enables real-time and accurate disease detection in complex rice field environments. However, conventional object detection models face challenges in direct deployment on embedded edge devices due to their high computational demands. To address this issue, this paper proposes a lightweight rice blast detection model based on YOLOv8n. First, a Star Operation is introduced to optimize the C2f module in YOLOv8, enhancing the model’s ability to extract multi-scale features of rice blast disease. Second, a lightweight High-level Screening Feature Pyramid Network (HSFPN) is adopted to improve feature fusion efficiency, and a detection head incorporating shared parameters and Detail Enhancement Convolution (DEConv) is designed to reduce computational costs while improving detection accuracy. Finally, the LAMP pruning algorithm is applied to remove redundant parameters, further lightweighting the model. Experimental results demonstrate that, compared to YOLOv8n (3.01M parameters, 8.1 GFLOPs), the proposed model reduces parameters by 88% (from 3.01M to 0.36M) and decreases computational cost by 59.2% (from 8.1 GFLOPs to 3.3 GFLOPs), while maintaining an mAP@50 of 78.6% (an improvement of 0.2%). Furthermore, the model achieves a real-time performance of 22.5 FPS on the Jetson Nano, providing practical value for rice blast disease detection and rice crop protection.