Всероссийский научно-исследовательский институт физиологии, биохимии и питания животных – филиал Федерального государственного бюджетного научного учреждения «Федеральный научный центр животноводства – ВИЖ имени академика Л.К. Эрнста»
ABSTRACT. Application of precise methods for forecasting individual productive values, fertility, the length of productive life of cows, and optimizing herd management are promising areas for improving the efficiency of modern productive animal husbandry. Practical application of electronic micro-sensors systems with wireless data transmission, which provides real-time data acquisition, caused the need for effective analysis and computer processing of the received information. The review considers a number of applied problems in the field of animal husbandry, in which the methods of machine classification and learning have found a successful application. In the arsenal of machine learning there are methods for identifying the most significant factors, establishing hidden dependencies, using a priori information and information from outside sources. At present, algorithms for automatic identification of individual animals, their behavior and state on grazing, prediction of physical and physiological parameters, prediction of breeding value, etc. have been developed based on methods of classification and restoration of dependencies. Main sections of the review: individual identification, classification of behavior and detection of heat stress; the identification of behavioral and physiological characteristics; prognosis of productive indicators; substantiation of decisions on culling; diagnosis and evaluation of the effectiveness of treatment of mastitis and respiratory diseases; assessment of the effectiveness of insemination; selection by performance indicators and genomic estimates; selection based on the conversion of feed energy into production; selection on fertility and survival indicators. The wide range and cost-effectiveness of the problems being solved testify to the high potential of machine learning and methods of data analysis in solving practical problems of animal husbandry.
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