Scientific Journal of KubSAU

In this article the general form of the formulated prob-lem of the creation of the scientific-informed and ef-fective tool for forecasting dynamics of sunflower yield in the areas of the Krasnodar region and in the whole region. We have proposed and substantiated the possibility to predict scenario of sunflower yield through the application of artificial intelligence tech-nologies, in particular, of the method of system-cognitive analysis

In this article, for the first time, the synthesis and veri-fication of the system-cognitive model of artificial ecosystems of sunflower crops in the Krasnodar region (at the levels of regions and in the whole region) are carried out. On the basis of the developed models, there are solved tasks: 1. Forecasting scenario of sun-flower yield for the period from 1 to 5 years. 2. The scientific study of artificial ecosystems of sunflower crops in the Krasnodar region (at the levels of regions and in the whole region)

In the article we have offered the technology and the methodology for the formulation and the solution of the problem of forecasting scenarios of changes in yield sunflower seeds at the level of a region and its districts, on the basis of the system-cognitive model that is different from the traditional: a high degree of formalization of the model of knowledge; the possibil-ity of the synthesis matrix transfer function of the object of forecasting directly on the basis of empirical data; correct work with incomplete (fragmented) and noisy data. For the first time, the study of the system-cognitive model of artificial ecosystems of sunflower in the Krasnodar Region, which is correctly regarded as the study of the ecosystem, as the verification of this model has shown its high adequacy has been conduct-ed