AI DRIVEN FISH DISEASE DETECTION FOR ACCURATE DISEASE IDENTIFICATION IN AQUATIC LIFE

Abstract

Accurate classification of fish diseases is crucial in the aquaculture industry, particularly in India, where seafood farming plays a vital role in the national economy. Traditional disease detection methods primarily depend on manual inspection by aquaculture experts through visual observation. These conventional approaches often suffer from limitations such as time consumption, subjectivity, and reliance on experiential knowledge, which can result in inconsistent and delayed diagnoses. To overcome these challenges, the proposed research focuses on developing an AI-driven automated system that leverages real-time monitoring using cameras and sensors to assess fish health. By integrating advanced image processing techniques with machine learning algorithms, the system aims to significantly enhance the precision and speed of disease identification in aquatic life. The implementation of smart aquaculture technologies marks a transformative shift in disease management strategies. These intelligent systems not only enable early and accurate detection of infections but also support sustainable farming by improving productivity, minimizing environmental impact, and ensuring better fish welfare. Through real-time data analysis and automation, the proposed approach contributes to a more resilient and efficient aquaculture ecosystem.