Intelligent support system for agro-technological decisions for sowing fields

The author is a member of the editorial board, and without taking part in the review process, decisions were made prior to the publication of this article.

Authors

  • Olena Fedusenko Faculty of Information Technology, Taras Shevchenko National University of Kyiv, Ukraine Author https://orcid.org/0000-0002-5782-5922
  • Natalia Shkurpela Faculty of Information Technology, Taras Shevchenko National University of Kyiv, Ukraine Author
  • Iryna Domanetska Faculty of Information Technology, Taras Shevchenko National University of Kyiv, Ukraine Author https://orcid.org/0000-0002-8629-9933
  • Anatoliy Fedusenko Military unit-К1410, Ukraine Author https://orcid.org/0000-0002-5782-5922

DOI:

https://doi.org/10.17721/AIT.2021.1.02

Keywords:

intelligent system, precision farming, cluster analysis, agro-technological solutions.

Abstract

The are crop planning problems exist in a modern agriculture of Ukraine. With the help of the intelligent support system for agro-technological decisions proposed by the authors, it is possible to simplify the planning process by using the concept of precision farming. Modern fields monitoring methods were analyzed and methods that will be used in the intelligent system are identified. The k-means method is one of them and will be applied to field clustering. The authors analyzed modern research and publications related to the concept of precision farming and the problem of implementing modern innovative information systems in agriculture of Ukraine. The decomposition of the intelligent system was carried out. Six main subsystems were identified, functional requirements were developed for each of them. Modern methods of fields monitoring are analyzed and methods that will be used in the intelligent system are identified, one of which is the k-means method, which will be applied to field clustering. Based on the already developed requirements, the authors have developed the general architecture of the system. The notation TOGAF was applied for the graphical display of the architecture. Based on the proposed architecture, intelligent system software was created. As a result of testing the soft-ware of the intelligent system, it is possible to draw a conclusion about its efficiency and readiness for implementation. The designed and developed system allows to carry out intellectual analysis of historical data of crops, to display results in the form of tables and graphs, to carry out planning of crops, agrotechnological operations and fertilizer application. The introduction of this system will improve the quality of management decisions and productivity of agricultural activities.

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References

Fedirets O.V. “Management of innovations in the implementation of precision farming technologies in Ukraine”, Scientific works of Poltava State Agar Academy, vol. 3, № 2 (7), p.302-308, 2013

Zelisko N. Melnyk V. “Development of innovative potential of the agricultural sector of Ukraine’s economy”. Bulletin of Lviv National Agrarian University. Ser. Economics of agro-industrial complex. 2018. Vip. 25. pp. 40-43.

Tiziano Gomiero. (2019) Soil and crop management to save food and enhance food security. [Online]. Available: https://doi.org/10.1016/B978-0-12-815357-4.00002-X

E.C.Leonard. (2015) Precision Agriculture. Reference Module in Food Science [Online]. Available: https://doi.org/10.1016/B978-0-08-100596-5.00203-1

Diego de la Rosa, Francisco Mayol, Elvira Díaz-Pereira, Miguel Fernandez, Diego de la Rosa. (2004) A land evaluation decision support system (MicroLEIS DSS) for agricultural soil protection: With special reference to the Mediterranean region. [Online]. Available: https://doi.org/10.1016/j.envsoft.2003.10.006

D. de la Rosa, F. Mayol, E. Diaz-Pereira, M. Fernandez and D. de la Rosa Jr. (2003) A land evaluation decision support system (MicroLEIS DSS) for agricultural soil protection. 2003. [Online]. Available: https://doi.org/10.1016 /jenvsoft.2003.10.006

(2018) Requirement types. QAInfo [Online]. Available: https://www.quality-assurance-group.com/requirement-types/

Chubukova I.A., Data Mining, M .: Internet University of Information Technologies, BINOM. Knowledge Laboratory, 2006.

I.I. Glagoleva, A. Y. Berko “Application of cluster analysis for data processing of land cadastre”. Bulletin of Lviv National University “Lviv Polytechnic”. Information systems and networks. 2014. Vip.743. Pp. 420-429

Nathan Landman, Hannah Pang, Christopher Williams,Eli Ross. (2017) k-Means Clustering. [Online]. Available: https://brilliant.org/wiki/k-means-clustering/

Vitlinsky V.V., Hrytsiuk P.M. “Study of winter wheat yield dynamics for the regions of Ukraine”. Modeling and information systems in economics: Coll. scientific works. 2007. Vip. 76. S. 275—295.

Gorban A.N., Zinovyev A.Y. «Principal Graphs and Manifolds», Ch. 2 in: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global. Hershey. PA. USA.,p.28-59 2009.

Gritsyuk P.M. “Forecasting grain yields: features and methods”, Scientific notes coll. Science. pr. SHEI “Kyiv National Economic University named after Vadym Hetman”, №11, p. 294-300, 2009

Yakymchuk V.G., Zholobak G.M., Porushkevych A. Y., Sakhatsky O.I. “Use of space and meteorological data to estimate the yield of winter wheat”, Space Science and Technology, Vol. 17, № 5, p. 64–67,2011

A.S. Zaryshnyak, Optimization of fertilizer and soil fertility in crop rotations, Kyiv: Agrarian Science, 2015.

S.A. Balyuk, B.S. Nosko, V.V. Shimel, L.V. Yeterevska, G.F .Momot “Optimization of plant nutrition in the system of factors of effective soil fertility”, Bulletin of Agrarian Science, Vol.7, №3, p.12-19,2019

(2021). State Statistics Service of Ukraine [Online]. Available: http://www.ukrstat.gov.ua/

(2021). Harvest online [Online]. Available: https://latifundist.com/urozhaj-online-2021#

Published

2021-11-04

Issue

Section

Applied information systems and technology

How to Cite

Intelligent support system for agro-technological decisions for sowing fields: The author is a member of the editorial board, and without taking part in the review process, decisions were made prior to the publication of this article. (2021). Advanced Information Technology, 1, 15-22. https://doi.org/10.17721/AIT.2021.1.02

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