(NHCE, 2023) Akash S INH201SO10 Aruna K INH20ISO17 Ellury Sai Vamshi Krishna INH201S048 Harshitha NM INH20ISO59
In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an
important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality
and economic loss. However, low-quality underwater images and low-identification targets present great
challenges to diseased fish detection. To overcome these challenges, this paper proposes a diseased fish
detection model, using an YOLOV5 network for aquaculture.