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.
Index Terms—Precision farming, soft sensors, predictive models, knowledge, associative search algorithms.
Natalia N. Bakhtadze, Evgeny M. Maximov, and Natalia E. Maximova are with Identification laboratory, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
L. N. Kozlovskaya is with Moscow Timiryazev Agricultural Academy, Moscow, Russia (e-mail: email@example.com).
Cite: Natalia N. Bakhtadze, Evgeny M. Maximov, Natalia E. Maximova, and Lamara N. Kozlovskaya, "Predictive Models for Agricultural Management," International Journal of Environmental Science and Development vol. 12, no. 7, pp. 220-225, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).