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COMPUTER SCIENCE
Intelligent management systems for digital farming. Part 2
N. Bakhtadzea, E. Maximova, N. Maximovaa, D. Donchana, D. Kuznetsovb, E. Zakharovc a V. A. Trapeznikov Institute of Control Science of Russian Academy of Science, Moscow, Russia
b National University of Science and Technology NUST MISIS
c Moscow Institute of Physics and Technology, Moscow, Russia
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.
Citation:
N. Bakhtadze, E. Maximov, N. Maximova, D. Donchan, D. Kuznetsov, E. Zakharov, “Intelligent management systems for digital farming. Part 2”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2020, no. 2, 99–111
Linking options:
https://www.mathnet.ru/eng/itvs413 https://www.mathnet.ru/eng/itvs/y2020/i2/p99
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