Feeding intensity assessment of aquaculture fish using Mel Spectrogram and deep learning algorithms

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.