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
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.
The animal carcass is one of important indicators of the development of young cattles, therefore it is essential to follow it up. Only the animals with sufficient body frame and with well-muscled top can be successfully fattened to high body mass. In this work we propose a prototype of non-intrusive scanning system for recovery of live cattle 3D shape with three depth cameras. To obtain the highest precision in measuring of cattle shape, we use calibrated cameras, curve fitting algorithms for solving the problem of missing data owing to partial occlusion, and algorithms for accurate fusion of point cloud data from three cameras. The measured animal 3D shape can be used, for instance, for automatic and precise estimation of body dimensions of live animals and for predicting the body weight of individual cattle as well as for daily monitoring production capacity of cattle.
The chemical composition analysis of average meat samples and M. longissimus dorsi testify that the greatest nutrients variability characterizes fat, the protein and mineral substances of carcasses edible part possess relative stability. In the study of qualitative structure of slaughter products the general regularity was revealed -increase of dry matter and fat contents and decrease in moisture with age. The process of fat deposition in the pulp of carcasses of compact body type genotypes was more intensive. That led to the maximum size (38,04%) of dry matter contents. Intensive accumulation of adipose tissue in compact body conformation group of bull-calves already had began with one-year-old age and to 15-month age the protein and fat ratio reached 1:0,65. At the age of 21 months tall animals were the best protein and fat ratio: they had ratio 1:0.83 instead 1:1.28 and 1:0.99 at compact and the medium contemporaries. Compact bull-calves had the highest of a forage's protein and energy expenses into own body nutrients, the smallest had tall contemporaries.
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