Abstract. 

The article presents an approach to the creation of information systems for digital farming, which allows more rational planning of land use, the use of fertilizers and fuel based on information technologies and intelligent forecasting models, which reduces the cost of production and increases the efficiency of agricultural production. In addition, a long-term agronomic and environmental effect can be achieved due to more gentle tillage and a decrease in the use of nitrogen fertilizers. The first sections of Part 2 of this article present methods for predicting the level of vegetation depending on the current values of key indicators and parameters of the selected mode. The following are the results of constructing intelligent identification models for forecasting prices for digital agricultural products. 

Keywords: 

digital farming, soft sensors, predictive models, knowledge management. 

PP. 99-110.

DOI 10.14357/20718632200209 

 

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