Deep learning-based appearance features extraction for automated carp species identification

Abstract

Fish species identification is vital for aquaculture and fishery industries, stock management of water bodies and environmental monitoring of aquatics. Traditional fish species identification approaches are costly, time consuming, expert-based and unsuitable for large-scale applications. Hence, in this study, a deep learning neural network as a smart, real-time and non-destructive method was developed and applied to automate the identification of four economically important carp species namely common carp (Cyprinus carpio), grass carp (Ctenopharingodon idella), bighead carp (Hypophtalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix). The obtained results proved that our approach, evaluated through 5-fold cross-validation, achieved the highest possible accuracy of 100 %. The achieved high level of classification accuracy was due to the ability of the suggested deep model to build a hierarchy of self-learned features, which was in accordance with the hierarchy of these fish’s identification keys. In conclusion, the proposed convolutional neural network (CNN)-based method has a single and generic trained architecture with promising performance for fish species identification.