Abstract: In order to realize efficient pig individual recognition, a pig recognition method based on machine vision was proposed. Training sets and test sets were made by collecting individual images of sows and piglets, and the images were expanded and screened. The identification model based on YOLO v3 was adopted and compared with the identification results of the Faster RCNN and SSD models. The results showed that the mean average precision of pig lets identification was 89.65%, the accuracy rate was 95.99%, and the recall rate was 84.09%. The mean average precision of sows identification was 95.16%, the accuracy rate was 96.00%, and the recall rate was 96.00%. The recognition rate of the model is 7 times higher than that of the Faster RCNN, and the average accuracy of the model is 9 percentage points higher than that of the SSD. The model reaches a high level in both the recognition rate and the recognition accuracy. This study can provide theoretical basis for intelligent identification, data monitoring and breeding informatization of pigs.