Identification of Feeding Intensity of Micropterus salmoides Based on Passive Underwater Acoustic Signals
To solve the problem of identifying the feeding intensity of high-density cultured Micropterus salmoides and achieve precise feeding of M. salmoides,this paper proposed a method for identifying the feeding intensity of school of M. salmoides based on feature of passive underwater acoustic signals. This method took the school of M. salmoides as the research object and used a feeding sound signal acquisition device to obtain the feeding sound signal of the school of M. salmoides. After preprocessing,multiple fea-tures of the underwater sound signal were extracted. The important features were selected through Ran-dom Forest,Pearson Correlation Analysis,and Principal Component Analysis,and a PSO-MLP fish school feeding intensity recognition model was established based on Particle Swarm Optimization and Multi-Layer Perceptron. The results showed that the PSO-MLP recognition model based on Principal Component Analysis feature selection had better recognition performance,with a classification recognition accuracy of 97.88％ and a recognition time of 6.24 seconds. Compared with the RF and the Pearson corre-lation analysis based PSO-MLP recognition model,the recognition accuracy was increased by 2.61％ and 1.02％,and the recognition time was shortened by 1.04 seconds and 1.09 seconds,respectively. This method effectively improved the accuracy and efficiency of fish feeding intensity recognition,and can pro-vide technical support for the development of intelligent feeding systems.
万方数据
维普
