With the global population growth and increasing demand for high-quality protein, aquaculture has experienced rapid development. Fish culture management and feed supply are crucial components of aquaculture. Traditional baiting management relies on experiential judgment and regular observation, which often leads to inefficient baiting practices and wastage. To address these issues, intelligent bait casting decisions have emerged. Leveraging advanced artificial intelligence algorithms, intelligent bait casting decisions can overcome most drawbacks of traditional bait management and enhance breeding efficiency. However, most of the current intelligent baiting decisions are focused on using methods such as image processing and target detection to identify different feeding actions and patterns. These methods do not discuss based on video streams and do not consider the changes in fish behavior during the baiting process. Therefore, we proposed a real-time analysis method based on the density distribution of fish feeding behavior (FishFeed). Firstly, this method upgrades the input mechanism, not only handling static images but also capable of real-time video stream analysis. Secondly, by evaluating the fish school density distribution through a new intelligent baiting strategy, this method can monitor the feeding behavior of fish school during the baiting process in real time. Finally, we constructed a dataset for fish school density analysis (DlouFishDensity) that includes a wealth of video and image frames, providing a valuable resource for research. Experimental results indicate that our algorithm outperforms MCNN, improving MAE by 1.63 and 1.35, MSE by 1.92 and 1.58, and reducing prediction time by 2.56 seconds on the same dataset. By implementing real-time analysis of fish feeding behavior density distribution, our method offers a more efficient and effective approach to baiting management in aquaculture, contributing to improved breeding efficiency and resource utilization.

中文翻译：

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基于养殖鱼类密度估计的实时投喂决策方法

随着全球人口增长和对优质蛋白质需求的不断增加，水产养殖业得到快速发展。鱼类养殖管理和饲料供应是水产养殖的重要组成部分。传统的诱饵管理依赖于经验判断和定期观察，这常常导致诱饵实践效率低下和浪费。为了解决这些问题，出现了智能诱饵投放决策。利用先进的人工智能算法，智能投饵决策可以克服传统饵料管理的大部分弊端，提高养殖效率。然而，目前的智能诱饵决策大多集中于使用图像处理和目标检测等方法来识别不同的喂食动作和模式。这些方法没有基于视频流进行讨论，也没有考虑鱼在诱饵过程中行为的变化。因此，我们提出了一种基于鱼类摄食行为密度分布的实时分析方法（FishFeed）。首先，该方法升级了输入机制，不仅可以处理静态图像，还可以进行实时视频流分析。其次，通过一种新的智能诱饵策略评估鱼群密度分布，该方法可以实时监测鱼群在诱饵过程中的摄食行为。最后，我们构建了一个用于鱼群密度分析的数据集（DlouFishDensity），其中包含大量视频和图像帧，为研究提供了宝贵的资源。实验结果表明，我们的算法优于 MCNN，在同一数据集上将 MAE 提高了 1.63 和 1.35，MSE 提高了 1.92 和 1.58，并将预测时间减少了 2.56 秒。通过对鱼类摄食行为密度分布进行实时分析，我们的方法为水产养殖中的饵料管理提供了更高效、更有效的方法，有助于提高养殖效率和资源利用率。