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Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2020, Issue 2, Pages 85–98
DOI: https://doi.org/10.14357/20718632200208
(Mi itvs412)
 

This article is cited in 1 scientific paper (total in 1 paper)

COMPUTER SCIENCE

Intelligent management systems for digital farming. Part 1

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 principles of creating a knowledge base and constructing models of grain yield depending on the regime of applying fertilizers based on intelligent identification algorithms, as well as models for predicting prices for digital agriculture products, have been developed.
Keywords: digital farming, soft sensors, predictive models, knowledge management.
Document Type: Article
Language: Russian
Citation: N. Bakhtadze, E. Maximov, N. Maximova, D. Donchan, D. Kuznetsov, E. Zakharov, “Intelligent management systems for digital farming. Part 1”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2020, no. 2, 85–98
Citation in format AMSBIB
\Bibitem{BakMaxMax20}
\by N.~Bakhtadze, E.~Maximov, N.~Maximova, D.~Donchan, D.~Kuznetsov, E.~Zakharov
\paper Intelligent management systems for digital farming. Part~1
\jour Informatsionnye Tekhnologii i Vychslitel'nye Sistemy
\yr 2020
\issue 2
\pages 85--98
\mathnet{http://mi.mathnet.ru/itvs412}
\crossref{https://doi.org/10.14357/20718632200208}
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