Enhancing Fish Disease Detection: A Comprehensive Review for Sustainable Aquaculture

Fish diseases pose a formidable challenge in aquaculture, exacting a toll in both lives and livelihoods. The urgency of timely disease identification and prevention cannot be overstated, as it underpins the very health and sustainability of fish farms while thwarting the perilous contagion of diseases. This comprehensive research overview paper delves deep into the realm of cutting-edge solutions, primarily harnessing the realm of computer vision and the domain of machine learning. Its focuses on the precise identification of specific fish diseases whose examples include Epizootic Ulcerative Syndrome (EUS), Ichthyophthirius (Ich), Columnaris along with more such ones. From the meticulous segmentation grounded in color analysis to the deployment of sophisticated deep learning architectures, these diverse methods are honed to combat each unique fish ailment. The outcomes etched in these studies are nothing short of remarkable, with select models attaining precision rates that soar above 99%. This compendium of research not only marks a momentous leap in the realm of automation for disease identification but also holds out a beacon of hope for the guardians of fish farms worldwide, promising a future where health and productivity harmoniously thrive.