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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 …