Deep Learning Based Export Oriented Guppy Fish Health Classification Method

Sri Lanka is one of the largest exporters of ornamental fish in the world. Guppies as a species are the largest portion of the total export volume. Meeting the health standards of the export destination is paramount and that matters a lot for the reputation of the country as a leading exporter. This research is concerned with the use of deep learning in the classification of healthy and unhealthy guppies. Specifically, transfer learning (TL) was utilized to retrain the YOLO model for this task. The study incorporated modern deep learning technologies in creating a model that will be effective in differentiating normal fish from the diseased. The created model was used for the test, and it yielded an accuracy rate of 90%, which proves the model’s efficiency for real-world applications. Moreover, the time taken in the identification of the diseased fish was greatly shortened by the automated method as compared to the manual approach, and that provides a practical solution for the industry. Besides improving the quality of the health assessment, integration of deep learning into the assessment of ornamental fish will also help to modernize the overall ornamental fish export industry.