## 4 Research on Backfat Thickness Detection Model Based on Sow Contour Images
### Interpretive Analysis of Backfat Thickness Detection Models  
The kinds of relevant features in the back images of 48 sows were counted, and it was found that the features in four sow image regions, namely, near the P2 point, near the body height point, the rump edge, and the flank profile edge of each sow, were extracted by the residual network. The following analyzes the effectiveness of the feature maps extracted by the residual network from the back image of one sow in predicting the backfat thickness of the sows to explain the phenomenon of the high accuracy of the model. The output feature maps of the residual module's second layer and the first convolutional layer's highlighted areas were roughly similar, so only the output feature maps of the first layer were analyzed. In the channel feature map of the first convolutional layer, shown in the first and third figures in Figure 6, the residual network extracted the sow back hind image feature in the red-circled area. The feature acted through the convolutional layer and the fully connected layer to output the backfat thickness. This feature reflects the information related to the sow backfat thickness, which is consistent with the location of the actual measurement of sow backfat thickness (P2). The highlighted area contains the location of the actual detection of sow backfat on the sow back, which indicates that there is a correlation between the features extracted by the network from the back driving region  
Figure 9: Feature map of the channel of the fourth layer residual module layer. The areas inside the red and yellow circles represent the relevant and irrelevant areas of the image of the sow’s back, respectively. of the sow and the sow backfat thickness, and that the residual network extracting the features in this region for the prediction of the backfat thickness is effective in predicting the backfat thickness. The channel feature map of the third convolutional layer and the channel feature map of the fourth residual module layer had roughly the same highlighted area corresponding to the sow's back area. It can be seen in the first and third graphs of the channel feature maps of the fourth residual module layer that the residual network extracted the features of the edge of the sow rump, and the features went through the action of the fully connected layer to output the sow backfat thickness, which reflects the information related to the characteristics of the sow rump. This had a greater impact on the network decision making, which is in line with the previous research carried out by [PERSON] et al. [28], who believed that there is a correlation between the height-to-width ratio of the sow rump, the area of the rump stock the radius of curvature, and the backfat thickness. These three areas also reflect the information related to the characteristics of the sow rump, and there is a correlation between the rump information and the sow backfat thickness, which suggests that the residual network extracting the features of the rump edges of sows is effective in predicting their backfat thickness. In the first and third graphs of the channel feature maps of the fourth residual module layer, the residual network extracted the features in the area near the sow body height point and output the backfat thickness after the action of the full connectivity layer, which influenced the decision of the residual network. This area coincides with the location where the actual measurement of sow backfat thickness was made--the thickest part of the shoulder [25]. [PERSON] et al. measured sow backfat thickness at this location, which indicates that the features extracted by the network near the sow's body height point are correlated with the sow backfat thickness, and the features extracted by the residual network from the region are effective in predicting sow backfat thickness.