Automatic Classification of Fish Species Through CNN-Based Image Analysis

Monitoring marine biodiversity, managing fisheries, and conserving sustainable ecosystems all depend heavily on the real-time identification of fish species. Convolutional Neural Networks (CNNs) combined with a live camera architecture are used in this study to propose a real-time fish species categorization system. The suggested approach uses a unique CNN architecture trained on a large-scale balanced dataset including 9,000 photos spanning nine species to classify fish species after capturing continuous video frames and performing preprocessing such as scaling and normalization. To improve generalization and lessen overfitting, the model uses dropout, batch normalization, and data augmentation approaches.Experimental assessment shows good precision and recall across most species categories, with an overall classification accuracy of about 92%on unseen data.Instantaneous prediction and visualization are made possible by the incorporation of real-time video recording, which qualifies the system for use in aquaculture settings, fish markets, and marine monitoring stations. The findings confirm the efficacy of automated categorization systems based on deep learning for scalable, dependable, and efficient real-time fish species identification.