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<p>The importance of aquatic plants in aquatic ecosystems is drawing growing attention, and accurate species identification is essential for advancing intelligent and precise ecological monitoring. Traditional methods fall short in large-scale, real-time monitoring, and while YOLOv8 is effective, it lacks sufficient lightweight optimization for mobile devices, limiting its practical application. Existing lightweight models also face challenges in balancing accuracy and speed in complex environments, such as dense growth, similar species, and occlusions. This paper introduces APlight-YOLOv8n, an enhanced YOLOv8n-based approach designed to address these challenges using the Faster Detect and Universal Inverted Bottleneck (UIB) modules. Evaluated on an aquatic plant dataset, APlight-YOLOv8n outperforms YOLOv8n: the mean average precision (mAP50) increased to 74.4%, a 1.9% improvement; the number of parameters (Params) was reduced to 2.74&#xa0;M, a 13.3% decrease; floating-point operations (FLOPs) dropped to 5.5G, a 32.9% reduction; and the inference speed (FPS) remained stable at 32.70. This model enables fast, accurate recognition in complex environments, providing efficient support for real-time field monitoring. In conclusion, APlight-YOLOv8n demonstrates superior performance in balancing accuracy and computational efficiency for aquatic plant detection and offers new insights for mobile ecological monitoring and broader smart environmental applications.</p>

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A real-time mobile aquatic plant recognition algorithm based on deep learning for intelligent ecological monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月12日

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  • Daoli Wang,
  • Zengchuan Dong,
  • Guang Yang,
  • Zhonglin Zhao,
  • Ranyu Liu,
  • Jitao Zhang,
  • Wenzhuo Wang,
  • Youwei Qin

摘要

The importance of aquatic plants in aquatic ecosystems is drawing growing attention, and accurate species identification is essential for advancing intelligent and precise ecological monitoring. Traditional methods fall short in large-scale, real-time monitoring, and while YOLOv8 is effective, it lacks sufficient lightweight optimization for mobile devices, limiting its practical application. Existing lightweight models also face challenges in balancing accuracy and speed in complex environments, such as dense growth, similar species, and occlusions. This paper introduces APlight-YOLOv8n, an enhanced YOLOv8n-based approach designed to address these challenges using the Faster Detect and Universal Inverted Bottleneck (UIB) modules. Evaluated on an aquatic plant dataset, APlight-YOLOv8n outperforms YOLOv8n: the mean average precision (mAP50) increased to 74.4%, a 1.9% improvement; the number of parameters (Params) was reduced to 2.74 M, a 13.3% decrease; floating-point operations (FLOPs) dropped to 5.5G, a 32.9% reduction; and the inference speed (FPS) remained stable at 32.70. This model enables fast, accurate recognition in complex environments, providing efficient support for real-time field monitoring. In conclusion, APlight-YOLOv8n demonstrates superior performance in balancing accuracy and computational efficiency for aquatic plant detection and offers new insights for mobile ecological monitoring and broader smart environmental applications.