N. Bakhtadze, E. Maximov, N. Maximova, D. Donchan, D. Kuznetsov, E. Zakharov Intelligent Management Systems for Digital Farming. Part 1
N. Bakhtadze, E. Maximov, N. Maximova, D. Donchan, D. Kuznetsov, E. Zakharov Intelligent Management Systems for Digital Farming. Part 1

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. 


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

PP. 85-98.

DOI 10.14357/20718632200208 

1. Vapnik V. Estimation of Dependences Based on Empirical Data. Springer, 2006.
2. A. Chervonenkis Reconstruction of conditional distribution field based on empirical data. In the book Soft Computing Systems (Design, Management and Application). Edited by Ajith Abraham, Javier Ruiz-del-Solar and Mario Koppen). IOS Press, 2002. Pp.462-469.
3. Bakhtadze, Natalia; Lototsky, Vladimir; Vlasov, Stanislav; Sakrutina, Ekaterina, Associative Search and Wavelet Analysis Techniques in System Identification // IFAC Proceedings Volumes. Series Title: System Identification, 2012. Vol. 16 | Part 1. Pp.1227-1232. ISSN: 1474-6670. ISBN: 978-3-902823-06-9.
4. Bakhtadze N., Pyatetsky V, Lototsky V. Knowledge- Based Models of Nonlinear Systems Based on Inductive Learning / New Frontiers in Information and Production Systems Modeling and Analysis: Incentive Mechanisms, Competence Management, Knowledge-based Production. Heidelberg: Springer, 2016. Pp. 85-104.
5. Bakhtadze N., Pyatetsky V, Sakrutina E.Predicting Oil Product Properties with Intelligent Soft Sensors // IFACPapersOnLine. 2017. Vol. 50, N1. Pp. 14632–14637.
6. Bakhtadze, N., Maximov, E., Donchan, D., Maximova, N.E., Kozlovskaya, L. INNOVATION AND KNOWLEDGE MANAGEMENT IN PRECISION FARMING // Proceedings of the 12th IADIS International Conference on Information Systems (Utrecht, Netherlands, 2019). Utrecht, Netherlands: International Association for Development of the Information Society, 2019. Pp. 265-270.
7. Artyushin A.M., Derzhavin L.M. A quick reference to fertilizers. M .: Kolos, 1984. - 208 p.
8. Smirnov P.M., Muravin E.A. Agrochemistry. M .: Kolos, 1977.240 s.
9. Gubanov Y. V., Ivanov N. N. Winter wheat. M .: Agropromizdat, 1988.330 s.
10. Prosyannikova O.I., Prosyannikov V.I. Coefficients of use of phosphorus and potassium from the soil by grain crops // Agrochemical Bulletin. 2009. No. 6. S. 7-8.
11. Pigorev I.Ya., Semykin V.A. The content of nutrients in plants and their removal with a harvest of winter wheat // Modern problems of science and education. 2007. No. 2. P. 38-40.
12. Radikorskaya V.A., Fokin S.A. The effect of doses and the ratio of mineral fertilizers on the growth and development of spring wheat // Far Eastern Agrarian Bulletin. 2010. No. 1 (13). S. 14-17.
13. Варламов В.А. и др. Вынос NPK пшеницей и ячменем на дерново-подзолистой тяжелосуглинистой почве ЦРНЗ РФ // Плодородие. 2012. № 2. С. 12-14
14. Zinkovskaya T.S. The coefficient of nitrogen utilization of fertilizers by winter rye and barley on sod-podzolic drained soil // International Scientific Journal. 2015. No 3 (34), Part 2. P. 20-21
15. Djuin G.P., Djuin A.G. The utilization rates of nitrogen, phosphorus and potassium from mineral fertilizers, manure and soil by crop rotation crops // International Journal of Experimental Education. 2016. No. 5. P. 83-90.

2020 / 02
2020 / 01
2019 / 04
2019 / 03

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".