Judgment Model of Cock Reproductive Performance based on Vison Transformer

Abstract

With the improvement of people's living standards, the demand for poultry has further increased. The screening of cock reproductive performance according to semen quality has become one of the attention directions. It is time-consuming to screen the breeding performance of cocks based on human vision, and there will be recognition errors. In this study, combined with the hypothesis that there is a correlation between cockscomb characteristics and semen quality, a scheme based on computer vision is proposed to avoid this problem. The collected cockscomb data are input into our model, which can automatically judge the breeding performance of cocks. We use transfer learning and change the weight ratio of the latest fine-grained visual classification algorithm Transfg at different depths to improve the accuracy of our data set. The average accuracy of the model in the test set is 44.6% (the data set contains 2053 pictures in the training set and 505 pictures in the test set), which is 1% better than the original vision transformer and 1.3% better than the convolution network model.