A Method for Eliminating Pig Face Recognition Errors Caused by Too Short Pig Growth Cycle

: In the process of modern large-scale pig breeding, it is necessary to distinguish the identity of each pig and real-time detect its health status, weight change, dietary status, and other parameters. Traditional methods waste a lot of resources, while the high quality of pork cannot be effectively guaranteed. This project is based on convolutional neural networks to design and develop a pig face recognition system. This system uses an overhead camera suspended above the pig house to monitor the pig house for 24 hours and identify and track each pig. Due to the rapid growth cycle of the pig, the facial image information changes rapidly, which has a significant impact on the pig face recognition model. The acquisition camera module is designed to correct monitoring and tracking data. The acquisition camera is installed in the necessary place of the pig every day to collect real-time information and upload the collected information to the server. By comparing the data of individual pigs at different developmental stages with the server, the identification information of individual pigs is determined, and tracking data is corrected in a timely manner. At the same time, the monitoring and identification screen is displayed on the screen, and behavioral information parameters are recorded to facilitate the information management and breeding of the farm.