Quantity Prediction of Mixed Freshwater Fish Based on Passive Underwater Acoustic Signals

Abstract

HighlightsThe use of passive underwater acoustic technology to estimate the species and quantity of freshwater fish provides a theoretical basis for effectively estimating the quantity of freshwater aquaculture.Mixed proportion recognition models for breams and crucians were built using probabilistic neural network (PNN) and support vector machine (SVM), and the influences of different super-parameters on the recognition rate were analyzed. The results showed that the classification model established with SVM after equiripple filtering was best.Mixed quantity prediction models for breams and crucians were constructed using multiple linear regression, and the effects of different filtering methods on the model performance were analyzed. The results showed that the best quantity prediction model was constructed with Butterworth filtering.Abstract. Acoustic signals of breams and crucians were collected at seven mixed proportions and 15 mixed quantitative gradients. After normalization and different filtering processes, the characteristics of the acoustic signals were extracted. Mixed proportion recognition models for breams and crucians were established using probabilistic neural network (PNN) and support vector machine (SVM). The results showed that the model established using SVM after equiripple filtering was best, and the recognition rate was 0.9583. A mixed quantity prediction model for breams and crucians was established by multiple linear regression based on ordinary least squares. The results showed that the model was best after Butterworth filtering, the adjusted decision coefficient of the model was 0.9514, and the relative analysis error was 4.7571. Keywords: Freshwater fish, Passive underwater acoustic signals, Pattern recognition, Regression analysis.