Study on classification models for acoustic signals of Litopenaeus vannamei feeding on different kinds of diets

We explored the classification of acoustic signals of L. vannamei feeding on different kinds of diets with machine learning techniques, so as to recognize the best model by comparing the performance among different classification models, and to provide references for the informatisation of feed management in shrimp aquaculture. We selected and processed the acoustic signals of L. vannamei feeding on nereid, pellet feed and squid. After noise reduction and filtering, two classification ways were employed: 1) Building models including Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (KNN) based on audio feature vectors. 2) Building Convolutional Neural Network (CNN) based on Mel-spectrograms. The results indicate that the CNN model, enhanced with Mixup data augmentation and Particle Swarm Optimization (PSO), achieved the highest accuracy of 91.67%. In addition, all four models achieved a recall rate exceeding 90% in identifying pellets, which indicates that the acoustic signals of shrimps feeding on pellets were more distinguishable than those associated with nereid and squid. The CNN model outperformed the traditional models in accuracy, precision, recall, and F1 score, exhibiting greater adaptability for analyzing complex acoustic signals with significant potential for its practical application.