Shrimp is a seafood that was fed popularly and high commercial value in Vietnam and specially in Kien Giang province. In adults, shrimp often has some diseases, especially white spot disease, leading to damage to the whole crop. Scientists have developed various methods to identify shrimp diseases; however, existing methods are often complex and expensive.
To recognize the above diseases, we try to develop a fast and efficient alternative that is a deep learning model. This method helps people to know when the shrimp has a disease. There are many tools available for training deep learning model. In which, the YOLO model is given top priority for identification. There have been many versions, but YOLOv5 has demonstrated superior performance in certain cases compared to the previous version. In this paper, the author focuses on researching and proposing a model to diagnose and detect white spot disease in shrimp based on the YOLOv5 network model. Our study proposes the YOLOv5 network model as an accurate, easy, and low-cost method to detect white spot disease in shrimp.
