COMPUTATIONAL MODEL FOR FRESH WATER FISH DISEASE CLASSIFICATION USING DEEP LEARNING APPROACH

Abstract

Fish diseases significantly affect global aquaculture by reducing productivity, damaging stock quality, and causing major economic losses. To support early detection and timely intervention, this study proposes an automated freshwater fish disease classification system using the VGG16 deep learning architecture with transfer learning. A dataset of over 5,000 annotated images covering eight disease categories was used to develop a robust model capable of distinguishing visually similar infections. In addition, Google Gemini AI is integrated to generate disease-specific treatment recommendations, offering practical guidance for farm management. The model is deployed as a web-based platform, enabling real-time diagnosis and decision support. This integrated framework provides an accessible and scalable solution for improving fish health monitoring and promoting sustainable aquaculture practices.