Machine Learning-Based Fish Disease Detection in Aquaculture
Year: 2025
Type: conference-paper
Abstract: — With the growth of aquaculture comes the question of how to maintain the sustainability and health of ornamental fish and aquatic plants. Diseases spread quickly, causing large losses and great suffering to both hobbyists and aquarists. Our project aims to fill this gap by adopting machine learning technology to an Aquaculture e-commerce platform where users can diagnose diseases by looking at images of fish and plants. Using this feature, users will be able to upload images of their fish or plants that are showing signs of distress, such as abnormal spots, coloration, or fins and leaves that are damaged. A deep learning algorithm examines these photos, identifies potential diseases and provides clear advice on treatment or prevention. Via this service, we aim to fasten disease detection into a simple and quicker process for everyone. This initiative not only provides an informative addition to our platform but also advances current research on how technology can enhance aquaculture. As time passes, and more users engage with the system, it will learn and get better, becoming increasingly accurate and reliable. By combining the convenience of e-commerce with the power of AI-driven disease detection, we hope to create a safer, healthier environment for aquatic life and a more enjoyable experience for people who care for them.
Authors: Anees, Ashmi, Jose, Amal K
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6968086959
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