Abstract: The Fish health is a critical factor in the success of aquaculture. Timely detection of diseases is essential to prevent the rapid spread of infections, minimize fish mortality, and reduce reliance on antibiotics. Traditional methods of disease detection rely heavily on manual inspection, which is time-consuming and prone to human error. This project proposes an automated system for fish disease detection using image-based machine learning techniques. By leveraging computer vision and deep learning algorithms, the system aims to efficiently identify diseases in fish through image analysis, offering a faster, more reliable alternative to traditional diagnostic methods. The system uses convolutional neural networks (CNNs) for classifying fish images into categories of healthy and diseased states. The dataset consists of fish images exhibiting various symptoms of diseases like white spot disease, fungal infections, fin rot, and bacterial gill disease. By training the CNN on these labeled images, the model can accurately predict the health status of fish in real-time, offering significant improvements in aquaculture management. The rapid growth of aquaculture as a global food production sector has increased the need for efficient and effective fish health management. Diseases in fish can lead to significant economic losses due to mass mortality, reduced production efficiency, and the use of antibiotics and other chemicals. Early and accurate detection of fish diseases is crucial for minimizing such risks. Traditional methods of disease diagnosis in aquaculture, which often rely on manual inspections by experts, are labor-intensive, time-consuming, and prone to errors. This study proposes a solution to automate and enhance the disease detection process through the use of image-based machine learning techniques, specifically employing deep learning algorithms like Convolutional Neural Networks (CNNs) for classifying fish diseases.
Keywords: Fish Disease Detection, CNN, Deep Learning, Aquaculture, Health Management.
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DOI:
10.17148/IJARCCE.2025.14687
