Calf Robust Weight Estimation Using 3D Contiguous Cylindrical Model and Directional Orientation from Stereo Images

Calving interval is often used as an indicator for fertility of beef cattle, however, maternal abilities are also required because the value of breeding cows depends on how efficiently the healthy and growing calves are produced. The calf's weight has been used as an indicator of maternity ability since the past few decades. We propose a method to estimate body weight by modeling the shape of calf using 3D information extracted from the stereo images. This method enables to predict the swelling of the cattle's body by creating a 3D model, which cannot be obtained solely from a 2D image. In addition, it is possible to estimate robust weight regardless of different shooting conditions toward cattle's posture and orientation. An image suitable for estimation is selected from motion images taken by the camera installed in the barn, and 3D coordinates are calculated by the images. Then, only the body is developed with a 3D model as it has the highest correlation with the body weight. Considering that the side of cattle's body may not be exactly perpendicular to the camera's shooting direction, a symmetric axis is extracted to find the inclination of cattle body from the camera in order to generate a 3D model based on the symmetric axis. 3D contiguous cylindrical model is used for the body of a cattle which has a rounded shape. In order to manipulate the shapes of the cylindrical surface, the circle and ellipse fittings are applied and compared. The linear regression equation of the volume of the cylindrical model and the actually measured body weight are used to estimate the cattle weight. As a result of modeling with the proposed method using the actual camera images, the correlation coefficient between the body weight and the model volume was at the best value, 0.9107. Even when experimentally examined with the different 3D coordinates obtained from other types of camera, the MAPE (Mean Absolute Percentage Error) was as low as 6.39%.