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).