用视觉与物联网技术精准喂养罗非鱼，提升产量58倍
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
- 结合视觉与传感器数据实时估算鱼体大小和数量
- 基于YOLOv8实现94%精度的鱼重预测
- 适合智能养殖、农业科技从业者参考
传统水产养殖常因投喂不精准导致环境问题和产量低下。本文提出一种融合计算机视觉与物联网技术的罗非鱼精准喂食系统。通过实时物联网传感器监测水质参数，利用计算机视觉算法分析鱼体尺寸与数量，动态确定最优投饵量。采用YOLOv8进行关键点检测，从鱼长推算体重，在3,500张标注图像上达到94%精度；通过深度估计将像素测量转换为厘米单位，确保投喂计算准确。数据采集与推理条件一致，显著提升性能。初步估算该方法可使产量较传统模式提高最多58倍。模型、代码与数据集均开源。
原文摘要 · Abstract (English)
Traditional fish farming practices often lead to inefficient feeding, resulting in environmental issues and reduced productivity. We developed an innovative system combining computer vision and IoT technologies for precise Tilapia feeding. Our solution uses real-time IoT sensors to monitor water quality parameters and computer vision algorithms to analyze fish size and count, determining optimal feed amounts. A mobile app enables remote monitoring and control. We utilized YOLOv8 for keypoint detection to measure Tilapia weight from length, achieving \textbf{94\%} precision on 3,500 annotated images. Pixel-based measurements were converted to centimeters using depth estimation for accurate feeding calculations. Our method, with data collection mirroring inference conditions, significantly improved results. Preliminary estimates suggest this approach could increase production up to 58 times compared to traditional farms. Our models, code, and dataset are open-source~\footnote{The code, dataset, and models are available upon reasonable request.
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