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Wheat is the most economically important and valuable food crop cultivated in most regions of the world, and various diseases have a significant impact on yield parameters. Particular attention in wheat protection technologies from phytopathogens is given to rust, since yield losses, depending on the weather conditions of the season and the resistance of the sown varieties, can range from 30 to 100%. The article provides brief information on wheat rust diseases (yellow, brown, stem rust), as well as on current methods of their identification. Accurate and timely identification of rust pathogens is a key step in making decisions on application of plant protection products in the battle against diseases, which prevents their further development, spread and the occurrence of epiphytoties. The article describes the main method for identification and further record of yellow, brown, stem rust - this is a classic phytopathological study based on usage of human resources. The advantage of this method is its accuracy and versatility. Among the drawbacks, one should single out the labor intensity and the need for a staff of qualified phytopathologists. In view of intensive development of computer technologies and agriculture digitalization, the possibility of using machine vision based on programming of neural networks and their training in identifying the main causative agents of diseases is acquiring scientific and practical interest. A promising methodological approach to identification of phytopathogens when providing phytosanitary monitoring and algorithms used for training of neural networks and applied in machine vision technologies are presented.

WHEAT RUST IDENTIFICATION USING PHYTOPATHOLOGICAL RESEARCH AND MACHINE VISION TECHNOLOGIES