<p>The main objective of this study is to implement the YOLO model in detecting and identifying individual fish and fish parts and evaluate the accuracy and effectiveness of the YOLO model. The implementation of the model is suitable for fish farms and processing facilities. This is a pre-treatment step to monitor and evaluate fish quality by fully identifying the external parts of the fish. In the Mekong Delta region, in general, and aquaculture areas, in particular, checking quality and output is the first important step in evaluating fish quality, researching appropriate fish farming environments, and promptly monitoring and detecting changes and diseases in the farming environment for timely treatment. The dataset, which consisted of many images of different fish species originating worldwide, was used and trained using the YOLO model. The article explores the structure and operation of the model, as well as the preprocessing steps and adjustments of hyperparameters. The model has reliable results when Precision reaches 87.6%, recall reaches 87.2%, and mAP for all classes reaches 89.8%.</p>