Abstract
<jats:p>Optimization of technologies and operating modes of agricultural equipment plays a decisive role in increasing the efficiency of the Russian agro-industrial complex. The issues of increasing the sustainability of the agro-industrial complex (AIC) through the use of artificial intelligence (AI) and neural network technologies (NNT) were considered. The effectiveness of applying domestic optimization for the AIC was analyzed. Indicators with identification of the most working hypotheses by means of Bayesian optimization taking into account the features of agricultural machinery and equipment for the AIC were established. The most frequently used applied aspects of AI and neural network technologies of NNT for the AIC of the Kursk region were identified. The main hypotheses for calculating the efficiency were defined: H1 - fuel savings up to 15%; H2 - reduction of grain losses by 10-12%; H3 - reduction of emissions and fuel consumption up to 20%; H4 - increase in labor productivity up to 25%; H5 - reduction of energy costs by 30%. The main tools for increasing the efficiency of equipment and technologies were selected. Based on the forecasting of probabilistic processes during the implementation of promising technologies in the agro-industrial complex, the shares of the contribution of each hypothesis to the increase in the efficiency of the agro-industrial complex were determined: P(H_2│A)=0.118; P(H_1│A)=0.147; P(H_3│A)=0.196; P(H_4│A)=0.245; P(H_5│A)=0.294. With regard to 28 agricultural districts of the Kursk region, the comprehensive implementation of not one, but several innovations and productivity increases with a probability of 0.476 and energy resources are saved with a probability of 0.695. The importance of logistics in increasing labor productivity by up to 25% in the marketing of agricultural products and the role of the digital economy in Russia for solving logistics areas of sales are considered.</jats:p>