An approach to the application of cognitive modeling in intelligent monitoring and forecasting systems in agriculture

Currently, there is a need to improve the efficiency of monitoring and forecasting in the field of agricultural sciences, particularly with regard to the rational use of natural resources. This, in tum, requires the development and implémentation ofmodem forecasting methods to reduce the impact of emerging natural risks. One possible solution is the intégration of expert knowledge and observational data using the mathematical framework of cognitive analysis. The article proposes an approach to the use of cognitive modeling in intelligent monitoring and forecasting systems, enabling the simulation and tracking of complex interactions among climatic, biological, and management parameters. It also provides a quantitative assessment of the dependence of target indicators (such as crop yield, quality, production costs, and resource efficiency) on the impact of unfavorable natural and other factors. Based on the proposed approach, a prototype of a monitoring and forecasting system was developed, incorporating a cognitive analysis module used to simulate scenarios of agricultural system behavior. The numerical experiment provided data for assessing system behavior, such as changes in crop yield under the influence of various unfavorable factors. For each scenario, management decisions were formulated to mitigate the impact of these factors and maintain high productivity. The obtained results can be applied in modem decision support systems to improve the efficiency of environmental monitoring and to forecast the impact of natural risks on agricultural needs.