You are here: Home Аrchive of articles (from 2017 till now) 2026 №2 List of Article 05 Dimitrov V. P., Nurutdinova I. N., Borisova L. V., Chernyaev А. Т. Structure of the Information System for Solving the Problem of Adjusting Harvesters

PDF To download article.

DOI: 10.15507/2658-4123.036.202602.327-343

UDK 631.354.2:004

 

Structure of the Information System for Solving the Problem of Adjusting Harvesters

 

Valery P. Dimitrov
Dr.Sci. (Eng.), Professor, Head of the Department of Quality Management, Don State Technical University (1 Gagarin Sq., Rostov-on-Don 344000, Russian Federation), ORCID: https://orcid.org/0000-0003-1439-1674, Researcher ID: E-4908-2018, Scopus ID: 57195505958, SPIN-code: 5991-4140, This email address is being protected from spambots. You need JavaScript enabled to view it.

Inna N. Nurutdinova
Cand.Sci (Phys.-Math.), Associate professor, Associate professor of Department of Higher Mathematics, Don State Technical University (1 Gagarin Sq., Rostov-on-Don 344000, Russian Federation), ORCID: https://orcid.org/0000-0002-3375-1295, Researcher ID: HPF-3929-2023, Scopus ID: 57196043287, SPIN-code: 1139-1723, This email address is being protected from spambots. You need JavaScript enabled to view it.

Lyudmila V. Borisova
Dr.Sci. (Eng.), Professor, Head of Management and Business-processes Department, Don State Technical University (1 Gagarin Sq., Rostov-on-Don 344000, Russian Federation), ORCID: https://orcid.org/0000-0001-6611-4594, Researcher ID: E-4863-2018, Scopus ID: 7006547874, SPIN-code: 5718-9727, This email address is being protected from spambots. You need JavaScript enabled to view it.

Anton T. Chernyaev
Undergraduate Student, Don State Technical University (1 Gagarin Sq., Rostovon-Don 344000, Russian Federation), ORCID: https://orcid.org/0009-0001-6836-6522, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract
Introduction. The use of the capabilities of the design of modern grain harvesters is limited by the human factor, in particular, by the significant information load on the operator. To increase the efficiency of harvesting operations and automate the process of adjusting agricultural machinery, it is relevant to develop an information system for adjusting harvesters.
Aim of the Study. The study is aimed at developing an approach to solving the problem of optimal adjustment of adjustable parameters of the combine harvester working bodies through using the introduction of intelligent information systems.
Materials and Methods. The study subject is a decision support software system for preliminary adjustment of grain harvester adjustable parameters. To solve the problem, there were used the methods of system analysis, expert assessment, fuzzy simulation, and relational database design. Client-server architecture and an embedded Database Management System SQLite were used as the hardware and software basis.
Results. There has been proposed a hierarchical structure for the task of preliminary adjusting of agricultural machinery working bodies. This structure consists of layers and elements and is the basis for the software system architecture. The system can operate in two modes: an expert mode and user mode. The expert system knowledge base for adjustment is based on a remote relational Database Management System with the ability to store all data locally for expert work and relevant data selected by experts for user work. There has been collected and analyzed expert information, which is used to create an expert knowledge base. There have been considered various combinations of external factor values, for which a conclusion about specific values for adjustable parameters has been derived.
Discussion and Conclusion. The developed information system allows formalizing the preliminary adjustment process for the harvester adjustable parameters and reducing the dependence of decision-making on the operator subjective experience. The practical importance of the study is in the ability to use the system to support experts and users when selecting initial parameter values in changing conditions of harvesting. Future studies are relevant, because of the necessity of expanding the knowledge base in the area under studying, taking into account a greater number of factors, and developing mechanisms for automatic adjustment of parameters in real time.

Keywords: grain harvester, preliminary adjustment, production rules, expert knowledge base, hierarchical task structure, client-server architecture

Conflict of interest: The authors declare that there is no conflict of interest.

For citation: Dimitrov V.P., Nurutdinova I.N., Borisova L.V., Chernyaev А.Т. Structure of the Information System for Solving the Problem of Adjusting Harvesters. Engineering Technologies and Systems. 2026;36(2):327–343. https://doi.org/10.15507/2658-4123.26362.327-343

Authors contribution:
V. P. Dimitrov – formulating the basic concept of the study, interpreting the study results, preparing the initial version of the text and formulating the conclusions.
I. N. Nurutdinova – literary analysis, analyzing the expert data, selecting optimal models.
L. V. Borisova – linguistic description of the subject area of the study, modeling of fuzzy expert knowledge.
A. T. Chernyaev – creating a fuzzy production rules base, programming the task.

All authors have read and approved the final manuscript.

Submitted 29.07.2025;
revised 23.01.2026;
accepted 27.02.2026

 

REFERENCES

  1. Chaplygin M.E., Zhalnin E.V. Comprehensive Evaluation of Modern Combine Harvester Performance in Southern Russia. Agricultural Machinery and Technologies. 2024;18(2):47–54. (In Russ., abstract in Eng.) https://doi.org/10.22314/2073-7599-2024-18-2-47-2
  2. Shulakov A.G., Yudina E.M., Alatortsev A.D., Matsko S.V. Combine Harvester Threshers: Problems and Solutions. Izvestia Orenburg State Agrarian University. 2021;(1):107–111. (In Russ., abstract in Eng.) https://doi.org/10.37670/2073-0853-2021-87-1-107-111
  3. Maslov G.G., Yudina E.M., Rinas N.A. Increase in Grain Combine Harvester Performance and in Quality of Harvesting Grain Crops. IOP Conference Series: Earth and Environmental Science. 2022;949:012109. https://dx.doi.org/10.1088/1755-1315/949/1/012109
  4. Lipovskiy M.I. Increasing the Efficiency of Threshing in an Axial Rotary Combine. Tractors and Agricultural Machinery. 2017;84(4):45–50. (In Russ., abstract in Eng.) https://doi.org/10.17816/0321-4443-66292
  5. Pastukhov A.G., Bakharev D.N., Volvak S.F., Burnukin A.E., Shvydchenko S.A. Сoncept of Corn Threshing by a Rotary Threshing-and-Separating Unit with an Air Curtain of the Discharge Port. Agricultural Engineering. 2024;26(2):4–12. (In Russ., abstract in Eng.) https://doi.org/10.26897/2687-1149-2024-2-4-12
  6. Maslov G.G., Trubilin E.I. Revisited Grain Harvesters Parameters Substantiation and Effectiveness of Them. Agricultural Machinery and Technologies. 2016;(2):28–31. (In Russ., abstract in Eng.) Available at: https://www.vimsmit.com/jour/article/view/125 (accessed 15.07.2025).
  7. Tsarev Yu.A., Dzhigarkhanov D.G. Automation of Tuning System of Technological Process for a Grain Combine Harvester. Tractors and Agricultural Machinery. 2009;76(12):29–31. (In Russ., abstract in Eng.) Available at: https://journals.eco-vector.com/0321-4443/article/view/68773 (accessed 15.07.2025).
  8. Dimitrov V.P., Borisova L.V., Nurutdinova I.N. Method for Determining the Initial Values of the Adjustable Parameters of the Combine Harvester Cutting Unit. Engineering Technologies and Systems. 2022;32(4):552–566. (In Russ., abstract in Eng.) https://doi.org/10.15507/2658-4123.032.202204.552-566
  9. Špokas L., Steponavičius D., Butkus V. Reduction of Fuel Consumption of Two Rotors Axial Flow Combine Harvester. Journal of Food Agriculture and Environment. 2014;12(2):329–333. Available at: https://www.wflpublisher.com/Abstract/5151 (accessed 15.07.2025).
  10. Aldoshin N., Didmanidze O. Harvesting Lupinus Albus Axial Rotary Combine Harvesters. Research in Agricultural Engineering. 2018;64(4):209–214. https://doi.org/10.17221/107/2017-RAE
  11. Aldoshin N.V., Berdyshev V.Ye., Malla B. Determing Operating Modes of Axial-Rotary Combine Harvesters For Harvesting Mixed Crops. Vestnik of Federal State Educational Establishment of Higher Professional Education “Moscow State Agroengineering University Named After V.P. Goryachkin”. 2019;(3):17–22. (In Russ., abstract in Eng.) https://doi.org/10.34677/1728-7936-2019-3-17-22
  12. Jasper S.P., Zimmermann G.G., Savi D., Neto L.S., Kmiecik L.L., Sobenko L.R. Operational Performance and Energy Efficiency of Axial Harvesters with Single and Double Rotor Systems in Soybean Seed Harvest. Ciência e Agrotecnologia. 2021:e031720. http://dx.doi.org/10.1590/1413-7054202145031720
  13. Zhalnin E.V., Godzhaev Z.A., Florentsev S.N. Conceptual Principles of Intelligent Agricultural Machines in the Case of Combine Harvester. Agricultural Machinery and Technologies. 2017;(6):9–16. (In Russ., abstract in Eng.) https://doi.org/10.22314/2073-7599-2017-6-9-16
  14. Chen J., Ning X., Li Y., Yang G., Wu P., Chen S. A Fuzzy Control Strategy for the Forward Speed of a Combine Harvester Based on KDD. Applied Engineering in Agriculture. 2017;33(1):15. https://doi.org/10.13031/aea.11299
  15. Zhang K., Cao S., Shen H., Wang H., Xu X., Han T., et al. Wang Fuzzy Control System for Load Stability of Intelligent Combine Harvester. IOP Conference Series Materials Science and Engineering. 2018;439(5):052009. https://doi.org/10.1088/1757-899X/439/5/052009
  16. Li W., Zhang K., Lv G., Dai H., Zhang C. An Improved Fuzzy Logic Control Method for Combine Harvester’s Cleaning System. Automatic Control and Computer Sciences. 2022;(56):337–346. https://doi.org/10.3103/S0146411622040058
  17. Dimitrov V.P., Borisova L.V., Tugengold A.K., Nurutdinova I.N. A Technological Adjustment of Agricultural Machines Based on Fuzzy Logic. Mordovia University Bulletin. 2018;28(2):239–254. (In Russ., abstract in Eng.) https://doi.org/10.15507/0236-2910.028.201802.239-254
  18. Dimitrov V.P., Nurutdinova I.N., Borisova L.V., Papchenko A.A. Technology for Adjusting Working Tools of a Rotary Harvester Based on Fuzzy Modelling. Engineering Technologies and Systems. 2025;35(1):13–29. (In Russ., abstract in Eng.) https://doi.org/10.15507/2658-4123.035.202501.013-029
  19. German D.M., Adams B., Hassan A.E. Continuously Mining Distributed Version Control Systems: An Empirical Study of How Linux Uses Git. Empirical Software Engineering. 2016;21(1):260–299. https://doi.org/10.1007/s10664-014-9356-2
  20. Lazarenko E.N., Dorokhina G.V., Garkusha D.A. Analysis on Version Control Systems. Problems of Artificial Intelligence. 2023;(2):12–25. (In Russ., abstract in Eng.) Available at: http://paijournal.guiaidn.ru/ru/2023/2(29).html (accessed 20.07.2025).

 

Licensed under a Creative Commons
This work is licensed under a Creative Commons Attribution 4.0 License.

Details
Hits: 27