Visual features based automated identification of fish species using deep convolutional neural networks

Morphological based fish species identification is an erroneous and time-consuming process. There are numerous fish species and due to their close resemblance with each other, it is difficult to classify them by external characters. Recently, computer vision and deep learning-based identification of different animal species is being widely used by the researchers. Convolutional Neural Network (CNN) is one of the most analytically powerful tools in deep learning architecture for the image classification based on visual features. This work aims to propose a deep learning framework based on the CNN method for fish species identification. The proposed CNN architecture contains 32 deep layers that are considerably deep to derive valuable and discriminating features from the image. The deep supervision is inflicted on the VGGNet architecture to increase the classification performance by instantly adding four convolutional layers to the training of each level in the network. To test the performance of proposed 32-Layer CNN architecture, we developed a dataset termed as Fish-Pak and is publicly available at Mendeley data (Fish-Pak: https://doi.org/10.17632/n3ydw29sbz.3#folder-6b024354-bae3-460a-a758-352685ba0e38) . Fish-Pak contains 915 images with six distinct classes; Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla catla (Thala) and three different image views (head region, body shape, and scale). To ensure the superior performance of proposed CNN architecture, we have carried out the experimental comparison with other deep learning frameworks involving VGG-16 for transfer learning, one block VGG, two block VGG, three block VGG, LeNet-5, AlexNet, GoogleNet, and ResNet-50 on the Fish-Pak data set. Comprehensive empirical analyses reveal that the proposed method achieves state of the art performance and outperforms existing methods.