Enhancing Aquaculture Sustainability with Deep Learning Techniques: Fish Species Detection and Disease Diagnosis

In order to improve fish illness detection and fish species recognition two crucial facets of aquatic ecosystem management this study focusses on utilising deep learning techniques. This study investigates how deep learning may dramatically increase the precision and effectiveness of fish population monitoring by using cutting-edge neural network designs. The study's first section explores the categorisation of fish species, showing how convolutional neural networks (CNNs) can successfully differentiate between several species using visual characteristics like colour, texture, and form. Through the analysis of bulk data containing illness symptoms and fish health markers based on both deep learning and conventional machine learning models, the second module tackles the early diagnosis of fish diseases. The models are taught to identify microscopic patterns and anomalies that may signal the presence of illnesses in aquaculture, hence enabling prompt intervention and preventative actions. The results demonstrate how deep learning-based methods have the potential to completely transform fish population management, guaranteeing sustainability, disease prevention, and improved decision-making in aquatic resource conservation.