Ai solutions for digital diagnostics of diseases of grain crops (using the example of <i>Pyrenophora teres</i> of winter barley)

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The aim of the research is to justify the feasibility of using digital intelligent technologies in forecasting the development of spot blotch in winter barley. The AI solution developed is a binary decision tree capable of predicting scenarios of net blotch development: depressive, moderate, and epiphytotic. To configure the algorithm’s parameters from 2021 to 2023, field and laboratory experiments were conducted at the Federal Scientific Center for Biological Plant Protection. The data preparation involved several stages, including setting up field plots to create an artificial infection background, preparing an inoculum, sowing highly susceptible and resistant winter barley varieties, and artificial inoculation. The selected input factors included the observed degree of leaf damage, type of variety resistance, vegetation phase at the time of primary infection, average relative air humidity during the vegetation phase of infection. The total sample size comprised 144 observations. The trained model demonstrated high classification accuracy on both the training and test datasets, with an accuracy rate exceeding 96 %. Based on a statistical assessment of the factors influencing the development of spot blotch in barley, it is shown that the most influential factor is the current degree of leaf infection (74,3 %), followed by the average relative air humidity (11,9 %), the variety’s resistance to the disease (10,4 %), and the stage development during which infection occurred (3,4 %). The proposed solution holds significant practical importance as it provides new opportunities for the diagnostic process of spot blotch in winter barley. Among these are high diagnostic speed, accuracy in forecast predictions, and applicability in field conditions.