Fish Species and Disease Detection System Using Deep Learning-Based Object Detection Model

Abstract

In fish farms, the overuse of feed can lead to residue and fish excrement polluting the water quality environment, thereby increasing the probability of pathogen proliferation and disease incidence in fish. In order to minimize the occurrence of diseases, it is crucial to administer an appropriate amount of feed and manage breeding diligently while mitigating any stress factors affecting the fish. This study involves the development of a fish species and disease detection system, where models are trained to identify different types of fish and their diseases. The system is designed to be used by fish farmers, offering a user-friendly interface through the Web. In the model training, the YOLOv7 model demonstrated high performance, achieving over 0.9 accuracy in detecting fish species. Meanwhile, for fish disease detection, the YOLOv5l model exhibited overall superior performance. However, there was a limitation in the dataset for fish disease detection, with only a small number of samples available. To overcome this, the fish species and disease detection system, developed in conjunction with the YOLOv5l model, was incorporated into the web page. This system aims to help identify the species, disease status, and specific affected regions in the fish population.