In this paper, we first analyze the accuracy of 3D object reconstruction using point cloud filtering applied on data from a RGB-D sensor. Point cloud filtering algorithms carry out upsampling for defective point cloud. Various methods of point cloud filtering are tested and compared with respect to the reconstruction accuracy using real data. In order to improve the accuracy of 3D object reconstruction, an efficient method of point cloud filtering is designed. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed point cloud filtering algorithm.

Many studies have purposed in order to measure live animal body characteristics using RGB-D cameras. However, most of these studies were made only for specific body measurements in interactive manner. A deviation from the expected animal body characteristics can indicate ill thrift, diseases and vitality. Currently, the farm manager can measure the body characteristics manually. Manual measuring generally requires a lot of labor, and it is, therefore, time consuming and stressful for animals. In this work we propose a non-intrusive depth camera-based system for automatic measurement of various cattle body parameters such as linear and integral characteristics along directional lines and local areas, geodesic distances, perimeters of cross sections, etc

No abstract

No abstract

Various machine learning algorithms have been used to model and predict the body weight of Hereford cows. The traditional linear regression model and various machine learning algorithms have been used to develop models for the prediction of the body weight of Hereford cows. The dependent variables include body weight and independent variables include withers height, hip height, chest dept, chest width, width in maclocks, sciatic hill width, oblique length of the body, oblique rear length, chest girth, metacarpus girth, backside half-girth, and age measurements of 1500 cows aged 2–6 years of age. The performance of the models is assessed based on evaluation criteria of the coefficient of determination, the root mean squared error, the mean absolute error, the mean absolute percentage error. We used a concept of splitting data into training, testing and validation datasets to provide a robust method for modelling and predicting. The RandomForestRegressor algorithm was found to provide the best results for training and testing datasets. It was concluded that machine learning algorithms may provide better results than the traditional models and may help researchers choose the best predictors for body weight of animals.