ML-Driven Ornamental Fish Disease Detection
Year: 2025
Type: conference-paper
Abstract: — Fish ornamentation is a successful hobby, but one of its greatest challenges is maintaining the health of Aquarium fish . Diseases easily spread, leading to massive losses and suffering for hobbyists and breeders alike. This project addresses this need by applying machine learning technology to an online ornamental fish platform where users can diagnose disease in fish and plants from images. With this feature, customers can upload pictures of their fish or plants with signs of sickness, like strange spots, color, or torn fins and leaves. A model examines these photos, and looks for some common diseases, and provides diagnosis and some suggestions on prevention. With this service, we aim to speed up disease detection into a simple and faster process for everyone, from beginners in aquariums to professional aquarists. This project not only adds an educational contribution to our site but also builds on existing research on the use of technology to improve ornamental fishkeeping. The longer it operates, and the more users that utilize the system, the better it will become and the more accurate and dependable it will be. By merging the convenience of online shopping with the capability of AI-powered disease diagnosis, we aim to provide a healthier, safer habitat for aquatic life and a better experience for those who love them.
Authors: George, Jeswin, Chacko, Merin
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893144192
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