Fish Disease Detection Using Machine Learning

• Published in: 2024 International Conference on Science Technology Engineering and Management (ICSTEM) pp. 1 - 4
• Main Authors: A, Vasumathi, Rathore, Prathik Singh, E, Siva Bharathi, N, Vignesh, S, Harsith
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.04.2024
• Subjects: Artificial Intelligence; Convolutional Neural Network; Fish; Fish Disease; Inference algorithms; Machine learning; Machine learning algorithms; Medical diagnosis; Shape; Visualization
• DOI: 10.1109/ICSTEM61137.2024.10560522

The identification of fish diseases is a crucial component of managing fisheries and aquaculture, and it is thoroughly explored in this project journal paper. Reduced death rates and disease propagation can both be aided by early disease identification in fish. However, conventional techniques for finding fish diseases, like visual inspection and microscopy, can be cumbersome and unreliable. The application of artificial intelligence for fish disease diagnosis has gained popularity in recent years. Based on fish photos or videos, artificial intelligence algorithms can be trained to recognize fish diseases. Fish disease detection could become quicker, more precise and easier to access as a result of this. Lack of extensive, top-notch datasets of fish photos and videos is one of the major obstacles to building AI models for fish disease identification. However, numerous research teams are attempting to solve this problem. In order to train AI models for fish disease diagnosis, for instance, the Fish4Knowledge project is creating a sizable library of fish photos and videos. The variety of fish appearance is another difficulty in the development of AI models for fish disease detection. Depending on their species, age, and state of health, fish can have a variety of sizes, shapes, colours, and patterns. Because of this, it may be challenging for AI algorithms to tell apart healthy fish and diseased.