Experimental studies of a remote device for biometric identification of the pre-mastitis state of the udder of dairy cows

Analysis of development of dairy cattle breeding in the Republic of Belarus is presented. The problems of reducing milk productivity and the period of economic use of dairy cows as a result of mastitis disease are noted. The importance of timely detection of sick animals, as well as treatment of the inflammatory process of the udder in the early stages is noted. The results of theoretical and experimental studies of a mock-up sample of a biometric identification device for the pre-mastitis state of udder of dairy cows are presented. In the course of experimental studies, a significant influence of ambient temperature on the thermal imaging picture was revealed, based on which the values of the radiation coefficient for cows were determined. The final adjustment of the previously established temperature ranges was made when determining one or another form of mastitis, namely, within 32.0−36.3 ℃ – the range of normal temperatures, 36.4−37.7 ℃ – “Subclinical mastitis”, 37.8−39.0 ℃ – “Clinical mastitis”. The most significant factors and optimization parameter have been determined. The percentage of the useful area of the studied object is selected as the optimization parameter. Based on the processing of experimental data, a mathematical model is obtained, described by a regression equation in the form of a polynomial of the second degree. The rational parameters of the device for biometric identification of the pre-mastitis state of the udder of dairy cows were obtained, namely: the angle of inclination of the thermal imaging module αо – 2,7o, the focal length to the object l – 794 mm, installation height of the thermal imaging module h – 495 mm. Also, based on the conducted research, priority areas and opportunities for development of the domestic agro-industrial complex through creation of integrated systems for monitoring the physiological state of animals within the framework of development of digital automated technologies of a “smart” farm have been identified.

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