Automated Fish Species Classification Using Convolutional Neural Networks

Appropriate classification of species in fish is important for biodiversity conservation, management of fisheries, and ecological research. This paper introduces a novel deep learning method for the automatic classification of aquatic species using CNNs. Testing the proposed model on a large-scale and diverse fish dataset resulted in impressive classification accuracy, reaching 92%. The architecture is made to balance the computational cost and predictability. It uses more complicated techniques, such as data augmentation, batch normalization, and dropout, in order to generalize. The study demonstrated evaluation of the model on multiple categories of the fish species and showed that it is precise and reliable in classification. The results show a potential for using CNN-based models for fully automating fish classification tasks, providing a scalable tool with a high degree of reliability in monitoring marine and freshwater ecosystems. Future work includes incorporating real-time detection capabilities into the model and adapting the same design to classify species in video streams.