Automatic classification of the phenotype textures of three Thunnus species based on the machine learning SVM algorithm

Abstract

Tuna resources are an important part of China's pelagic fishery production. However, for China's tuna fishery, tuna species caught at sea are still manually classified, which is a time-consuming and inefficient process; so China's tuna fishery needs to develop toward automation. This study uses gray-level co-occurrence matrix (GLCM) and VGG16 to visualize phenotypic texture through local images of three Thunnus species. At the same time, texture feature index data (TFD), deep feature data (DFD), and their combined feature data (CFD) are obtained from texture images. Support vector machine (SVM) with different kernel functions is used to classify phenotypic texture of tuna automatically. The study shows that visualized texture images of different tuna using GLCM and VGG16 have biological characteristics. In the classification results without cross-validation, the average classification accuracy of TFD in polynomial was 83%, the average classification accuracy of DFD in RBF (Radial basis function) was 93%, and the average classification accuracy of CFD in RBF was 95%. It is concluded that tuna phenotype texture can be efficiently classified by using SVM with different kernel functions.