Assessing the intensity of fish feeding activity using fish feeding behavior can help farmers efficiently decide on the amount of feeding bait. However, accurate extraction of fish feeding behavior features is difficult because of the small area of fish in the image and the randomness of fish swimming. To address this problem, an improved MobileNetV3 network, namely multi-scale information fusion (MSIF)-MobileNetV3, was proposed for analyzing the fish feeding behavior. Specifically, MSIF is a novel channel attention module used to replace the Squeeze-and-Excitation (SE) module that improves the attention of the model to fish schools behavior in feeding images using spatial information integration and multi-scale feature fusion. To evaluate the effectiveness of the proposed method, its performance was compared with that of the MobileNetV3 network optimized using multiple training strategies and other classical convolutional neural networks. It was trained and tested using a self-built dataset, and the experimental results showed that the MSIF-MobileNetV3 network using a basic training strategy obtained an optimal classification accuracy of 96.4 % on the test set. Thus, by analyzing the feeding activity of fish, the proposed method can assist in the automatic selection of bait feed under factory farming conditions.

利用鱼类摄食行为评估鱼类摄食活动的强度可以帮助养殖者有效地决定摄食饵料的数量。然而，由于图像中鱼类的面积较小，且鱼类游动的随机性，准确提取鱼类摄食行为特征存在一定难度。为了解决这个问题，提出了一种改进的 MobileNetV3 网络，即多尺度信息融合 (MSIF)-MobileNetV3，用于分析鱼类摄食行为。具体来说，MSIF 是一种新颖的通道注意模块，用于替代挤压和激发 (SE) 模块，该模块使用空间信息集成和多尺度特征融合提高了模型在摄食图像中对鱼群行为的注意。为了评估所提出方法的有效性，将其性能与使用多种训练策略优化的 MobileNetV3 网络和其他经典卷积神经网络进行了比较。使用自建数据集进行训练和测试，实验结果表明，采用基本训练策略的MSIF-MobileNetV3网络在测试集上获得了96.4%的最优分类准确率。因此，通过分析鱼类的摄食活动，所提出的方法可以帮助在工厂化养殖条件下自动选择饵料。4% 在测试集上。因此，通过分析鱼类的摄食活动，所提出的方法可以帮助在工厂化养殖条件下自动选择饵料。4% 在测试集上。因此，通过分析鱼类的摄食活动，所提出的方法可以帮助在工厂化养殖条件下自动选择饵料。