On-Barn Forecasting Beef Cattle Production Based on Automated Non-Contact Body Measurement System

Simple Summary The aim of this study is to predict the productivity of beef cattle, using a systematic assessment of animals according to their main genetic parameters. Correlation analysis reveals that the main indices for the meat productivity prognosis are live weight and the measurements of animals taken at birth. Corresponding correlation coefficients were determined to predict animal body size at 18 months using measurements taken at birth. After our studies, it has been revealed that high positive correlation coefficients between individual traits (live weight and body measurements) indicate the expediency of using indirect selection. It has been established that three main indicators can serve as a forecast of meat productivity after slaughter in this population: the live weight of mothers, the live weight of animals at birth, and the indicators of animals at birth. The automated non-contact body measurement system using an RGB-D image capture system can be used to collect all three types of data that are necessary for accurate analysis. The main task of selective breeding is to determine the early productivity of offspring. The sooner the economic value of an animal is determined, the more profitable the result will be, due to the proper estimation of high and low productive calves and distribution of the resources among them, accordingly. To predict productivity, we offer to use a systematic assessment of animals by using the main genetic parameters (correlation coefficients, heritability, and regression) based on data such as the measurement of morphological characteristics of animals, obtained using the automated non-contact body measurement system based on RGB-D image capture. The usefulness of the image capture system lies in significant time reduction that is spent on data collection and improvement in data collection accuracy due to the absence of subjective measurement errors. We used the RGB-D image capture system to measure the live weight of mother cows, as well as the live weight and body size of their calves (height at the withers, height in the sacrum, oblique length of the trunk, chest depth, chest girth, pastern girth). Cows and cattle of black-and-white and Holstein breeds (n = 561) were selected as the object of the study. Correlation analysis revealed the main indices for the forecast of meat productivity-live weight and measurements of animals at birth. Calculation of the selection effect is necessary for planning breeding work, since it can determine the value of economically beneficial traits in subsequent generations, which is very important for increasing the profitability of livestock production. This approach can be used in livestock farms for predicting the meat productivity of black-and-white cattle. RGB-D camera; beef cattle; image analysis; machine learning; non-contact body measurement; precision livestock farming