CONFERENCE / ICCAIS-2026

The role of aquaculture in food security holds a large number and supports the economic value of the country, but fish disease detection is a complex task in present times and requires a huge amount of continuous monitoring. If fish diseases are not detected at an early stage, they can spread throughout the fish farm, which leads to heavy losses for farm owners, especially in large or remote farms where detecting or identifying diseases in fishes completely depends upon manual work and may not result in good accuracy. To overcome these limitations, this research paper proposes a smart aquaculture system for real-time fish disease detection using computer vision and deep learning along with Raspberry Pi integrated with a camera module, where a YOLO model is used for automatic detection of diseases such as fin damage and EUS. When a disease is detected, the system automatically sends an alert to the farm owner through notifications and activates a buzzer and displays the information on a display unit, which helps farmers take quick action and reduces disease-related losses.