IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species

Abstract

Fish is a very diverse animal of all vertebrate animal groups, of which there are more than 33,000 species in the world. There are different types of fish in the four major geographical regions of Turkey. Classification of different fish species is very important for aquaculture, stock management of water bodies, monitoring of aquatic organisms and conservation of marine biology. In the classification of fish, both knowledge and great effort are required to determine the characteristics of fish. Traditionally, however, manual classification of extrinsic characteristics of different fish species has been a difficult and time-consuming process due to their close resemblance to each other. Recently, deep learning methods used in the light of developments in the field of computer vision have facilitated the training of fish image classification models and the recognition of various fish species. In this study, a new evolutionary neural network model classifying 8 different fish species using deep learning methods was proposed. The proposed model is compared with the ResNet-50, ResNet-101 and VGG16 models. The success accuracies obtained as a result of the comparison are respectively; 98.12% in the proposed model, 91.37% in the ResNet-50 model, 86.12% in the ResNet-101 model and 97.75% in the VGG16 model. It has been observed that the proposed model classifies sea fish, which is widely consumed in our country, with higher performance compared to other models.