Accurately and objectively analyzing fish feeding intensity is essential to guiding feeding and production techniques. Fish feeding intensity in recirculating aquaculture systems (RAS) can be used to indicate a fish's appetite. However, the low efficiency and lack of objectivity of manual observation are problems with current fish feeding intensity assessment processes. Applying acoustic techniques to aquaculture issues is an insufficiently explored area that requires new investigations, particularly into methods that explore temporal information in acoustic spectrograms. With Oplegnathus punctatus as the experimental species, we proposed a fish feeding intensity detection method based on the Mel Spectrogram and MobileNetV3-SBSC lightweight networks. First, the Oplegnathus punctatus feeding sound dataset, which has a total of 3 types—"strong," "medium," and "none," was built. Next, Mel Spectrogram feature maps were extracted using steps including preprocessing, Fast Fourier Transform (FFT), Mel filter bank (MFB), etc. Finally, the MobileNetV3-SBSC lightweight network was used to detect and recognize the obtained feature maps. Experimental results indicated that the proposed MobileNetV3-SBSC model, as compared to the MobileNetV3-S model, improved test accuracy by 4.6% and decreased test loss by 67.4% with only a 0.84% increase in the number of parameters and a 3.08% increase in the model size. Additionally, we have verified that the accuracy of the test set was 59.6%, 53.3%, 83.3%, 85.3%, and 85.9% for groups of 5, 15, 40, 70, and 100 fish, respectively. This study demonstrated that the proposed method is not applicable to a small number of fish, which means that when the numbers of fish are small, changes in the feeding of individual fish would have a significant effect on the whole feeding feature map, leading to negligible changes in feeding features. However, in view of the commonly high aquaculture density, the proposed method can be used to automatically and objectively examine fish feeding, which could provide a theoretical basis and methodological support for further feeding decisions.

准确客观地分析鱼类摄食强度对于指导摄食和生产技术至关重要。循环水养殖系统 (RAS) 中的鱼类摄食强度可用于指示鱼类的食欲。然而，人工观察的低效率和缺乏客观性是当前鱼类摄食强度评估过程的问题。将声学技术应用于水产养殖问题是一个探索不足的领域，需要进行新的研究，特别是探索声谱图中时间信息的方法。以Oplegnathus punctatus为实验物种，提出了一种基于Mel Spectrogram和MobileNetV3-SBSC轻量级网络的鱼类摄食强度检测方法。一、Oplegnathus punctatus feeding sound数据集，共建有“强”、“中”、“无”3种类型。接下来，通过预处理、快速傅立叶变换（FFT）、梅尔滤波器组（MFB）等步骤提取梅尔谱图特征图。最后，使用MobileNetV3-SBSC轻量级网络对获得的特征图进行检测和识别。实验结果表明，与 MobileNetV3-S 模型相比，所提出的 MobileNetV3-SBSC 模型的测试准确率提高了 4.6%，测试损失降低了 67.4%，参数数量仅增加了 0.84%，参数数量增加了 3.08%。模型大小。此外，我们还验证了测试集的准确度分别为 59.6%、53.3%、83.3%、85.3% 和 85.9%，分别为 5、15、40、70 和 100 条鱼。这项研究表明，所提出的方法不适用于少量鱼类，这意味着当鱼类数量较少时，个体鱼的摄食变化会对整个摄食特征图产生显着影响，导致可忽略不计喂养特征的变化。然而，鉴于水产养殖密度普遍较高，所提出的方法可用于自动客观地检测鱼类摄食情况，为进一步的摄食决策提供理论依据和方法支持。