Scholarlink

<p>The rapid detection of fish diseases is crucial for the sustainable development of aquaculture, ensuring both economic viability and environmental protection. This study presents a novel real-time fish disease detection system based on tiny machine learning (TinyML) technology. By integrating the You Only Look Once 11 nano (YOLO11n) lightweight object detection model with a RISC-V microcontroller and hardware design, the system achieves efficient, low-power, and accurate disease detection tailored to resource-constrained aquaculture environments. The system incorporates edge computing to perform real-time disease detection locally, reducing reliance on cloud services and improving data security. Experimental results demonstrate the system’s effectiveness, achieving a mean average precision at IoU thresholds from 0.5 to 0.95 (mAP50–95) of 0.736 with robust performance in real-world scenarios. The lightweight architecture enables flexible deployment in various aquaculture conditions, from offshore environments to small-scale farms. This study underscores the potential of TinyML to revolutionize aquaculture management and promote the intelligent, automated monitoring of fish health.</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

Real-time rapid visual fish disease detection system based on tiny machine learning

  • 期刊论文

  • 发表日期：

    2025年10月10日

查看全文
  • Jiayi Wang,
  • Yihan Yin,
  • Jinqi Yang,
  • Feiyu Zhu,
  • Daoliang Li,
  • Yang Wang

摘要

The rapid detection of fish diseases is crucial for the sustainable development of aquaculture, ensuring both economic viability and environmental protection. This study presents a novel real-time fish disease detection system based on tiny machine learning (TinyML) technology. By integrating the You Only Look Once 11 nano (YOLO11n) lightweight object detection model with a RISC-V microcontroller and hardware design, the system achieves efficient, low-power, and accurate disease detection tailored to resource-constrained aquaculture environments. The system incorporates edge computing to perform real-time disease detection locally, reducing reliance on cloud services and improving data security. Experimental results demonstrate the system’s effectiveness, achieving a mean average precision at IoU thresholds from 0.5 to 0.95 (mAP50–95) of 0.736 with robust performance in real-world scenarios. The lightweight architecture enables flexible deployment in various aquaculture conditions, from offshore environments to small-scale farms. This study underscores the potential of TinyML to revolutionize aquaculture management and promote the intelligent, automated monitoring of fish health.