Abstract
This paper is devoted to the development of predictive models for decision support systems applied in precision farming. Application of predictive models makes it possible to use resources effectively, which reduces the cost of production and increases the efficiency of agricultural production. In addition, the forecast makes it possible to reach a long-term agronomic and ecological effect due to more careful tillage and reduced use of fertilizers. The algorithms using knowledge base for creating models of grain yield are described and the results of applying these models are presented.
Highlights
Agro-management today actively uses the capabilities of information technologies - both for the implementation of more effective management of a certain technological process, and for organizing the most profitable farming in general for specific agricultural enterprises, taking into account the specifics of their activities and the current situation.Digital farming is a high-tech approach to managing the state of fields and the efficiency of their use based on the study of the dynamics of their physical and agrochemical properties using modern mathematical and information technologies.Management within the concept of digital farming is based on the principle that a field that is heterogeneous in topography, soil cover or agrochemical content is subjected to heterogeneous cultivation
The results of modeling presented in this paper indicate that the main dynamic factors influencing the yield are climatic conditions and amounts of applied fertilizers
In the paper, the methods of creating highly effective algorithms for decision support systems used in conditions of fertilizers differential applying are represented
Summary
Agro-management today actively uses the capabilities of information technologies - both for the implementation of more effective management of a certain technological process, and for organizing the most profitable farming in general for specific agricultural enterprises, taking into account the specifics of their activities and the current situation. Based on the analysis of data characterizing features of the site, taking into account the peculiarities of soil types and climatic conditions, are carried out: planning of sowing, the calculation of the amount of fertilizer application, crop yield forecasting and financial planning. This approach allows more rational use of fertilizers and fuel, which reduces the cost of production and increases the efficiency of agricultural production. The algorithms have demonstrated high efficiency for non-linear non-stationary objects in industry and power engineering The use of such algorithms in agromanagement allows making effective management solutions in precise (coordinate) farming systems
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