# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 1 Introduction  
Periodic weight measurement can measure the growth rate of cattle, identify animals at target weight, and serve as a method for an indirect health checkup. However, because the weight of a cow after breeding is approximately 700-1100 kg, it is difficult for livestock farms to transport the cattle to a scale, and therefore, weight information cannot be easily gathered or actively used. According to the Korea Rural Economic Institute, the price of Korean beer has been rising steadily since 2015. Statistics Korea also explained that per capita beef consumption in Korea is increasing by an average of 4.3% per year due to an increased preference for meat. These increases in cattle, cattle prices, and beef consumption are the same worldwide. According to OECD Statistics, the production, consumption, and price of beef are all increasing [1]. Figure 1 shows the growth of the cattle livestock market in Korea and the world. Figure 0(a) shows the increasing number of livestock farms raising more than 100 cattle in Korea, Figure 0(b) shows the world's beef production and consumption, and Figure 0(c) shows the rise in global beef prices. Related studies that investigated the correlation between body type and body weight of cattle demonstrate the potential of predicting body weight using the body parts of cattle; this was the motivation behind conducting the current study. [PERSON] et al. [2] used Mask R-CNN to separate cattle and background. They used a histogram difference-based approach to select repetitive and similar images in the video. As a result of the experiments, they obtained a mean pixel accuracy of 0.92, which showed better performance thanSharpMask (0.82) and DeepMask (0.53). [PERSON] et al. [3] revealed that body height, chest width, and body depth had correlations of 0.65, 0.73, and 0.49, respectively, with body weight, and [PERSON] et al. [4] showed that body depth had a correlation of 0.49 with body weight. [PERSON] et al. [5] investigated the correlation between body height and body weight and obtained a correlation of 0.94. Some studies attempted to estimate the weight of animals using image-based methods. [PERSON] et al. [6]. attempted to estimate the weight of Girolando cattle using physical characteristics such as heart girth (HGP), abdominal circumference, and body length. [PERSON]'s correlation was used to estimate the correlation between the traits and weight. HGP had the highest correlation coefficient at 0.88, followed by the circumference of the abdomen at 0.79, hip width at 0.65, and body length at 0.58. As a result, weight estimation using a linear regression model showed an RMSE of 42.52 kg. [PERSON]. [7] attempted to estimate the weight of a cow using a Bayesian Ridge algorithm applied to RGB-Depth camera images. A threshold was applied to the histograms of the depth image and the HIS image to obtain a mask of cattle from the background. The mask was used for feature extraction relevant to weight estimation. As a result, a mean square error (MSE) of 1046.0 was obtained. [PERSON] et al. [8] obtained a mean absolute percentage error (MAPE) of 2.27% using a manually created segmentation mask to find the distances between points on the outline of the segmentation mask, and estimated the cattle weight using those values. [PERSON] et al. [9] used cattle top-view and side-view data and achieved an error rate of 5% to 10.7% using multiple regression equations. [PERSON] et al. [10] obtained a weight estimation error rate of 6.1% for predicting the volume of cattle using a 3D depth camera. [PERSON] et al. [11] predicted the weight of pigs with an error rate of 4% using a regression neural network and the area captured by the human eye in a top-view image of pigs. Using side-view data can further improve performance, but it is difficult to take this approach in livestock farms because of animal occlusions. In general, 3D cameras have a limited imaging distance so they capture a smaller area than 2D cameras, and are more expensive. We used 2D top-view images in this study.