摘要： 准确评定鱼类食欲强度是实现水产养殖精准投喂的重要手段，针对当前鱼类食欲强度评定存在的精度低、实时性差等问题，提出了一种基于改进MobileNetV2和迁移学习的鱼类食欲强度评定模型。首先，以MobileNetV2为基础网络结构，在倒残差块的跳跃连接后引入注意力机制CBAM模块，进一步提升了模型对关键特征信息的捕捉能力。其次，针对现场养殖环境，采集了鱼群摄食时的水面视频数据，通过提取关键帧并应用基于哈希值的差异阈值方法去除冗余图像，构建了一个真实养殖坏境的鱼类食欲强度数据集，并在该数据集上进行相关验证试验。结果显示，迭代200次后，改进后的最优模型性能优于其他模型，内存占用量仅为9.3MB，准确率为92.75%，召回率为92.92%，精确率为92.65%，F1分数为92.70%。研究表明，该模型具有较高的评定准确率，且内存占用量较小，为水产养殖的智能化和精准化提供了重要的参考和支持。

关键词: 水产养殖, 食欲强度, 深度学习, 图像分类

Abstract:  Accurate assessment of fish appetite intensity is essential for achieving precise feeding in aquaculture. To address the current issues of low accuracy and poor real-time performance in fish appetite intensity evaluation, this study proposes a fish appetite intensity assessment model based on an improved MobileNetV2 and transfer learning. First, we enhance the MobileNetV2 architecture by introducing the CBAM attention mechanism module after the skip connections in inverted residual blocks, thereby significantly improving the model's ability to capture key feature information. Next, we collect surface video data of fish feeding behavior in actual aquaculture environments. Through keyframe extraction and redundancy removal using hash-based differential threshold methods, we construct a real-world fish appetite intensity dataset. Validation experiments on this dataset show that after 200 iterations, the optimized model outperforms other models, with a memory footprint of only 9.3 MB, achieving 92.75% accuracy, 92.92% recall, 92.65% precision, and 92.70% F1-score. The proposed model offers high assessment accuracy with a compact memory footprint, providing significant technical support for intelligent and precise aquaculture management.

Key words: aquaculture, appetite intensity, deep learning, image classification