Fish disease detection using convolutional neural network (CNN) algorithm

Effective disease detection in aquaculture is crucial for maintaining fish populations and promoting best practices. Traditional methods often rely on visual inspection alone, which can lack precision and efficiency. This study introduces a fish detection system that leverages Convolutional Neural Networks (CNNs) and advanced image processing techniques, with a flexible, iterative research approach guiding its development. The CNN model, selected through algorithmic analysis, achieves an impressive 88.04% accuracy in automatically identifying and diagnosing various fish diseases. Trained on diverse datasets, the model can discern key features from fish images. An intuitive software application is then developed for aquaculture professionals, enabling rapid and accurate disease diagnosis. This approach marks a significant advancement in applying machine learning for disease management in aquaculture, overcoming the limitations of manual observation and contributing to the sustainable future of fish farming.