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Automated selection of agricultural technologies and tractor fleet of an agricultural enterprise: web-application structure and algorithms

https://doi.org/10.26898/0370-8799-2022-4-12

Abstract

   The issues of increasing efficiency and competitiveness of a crop farming enterprise through information support of production using digital technologies are considered. The process of selecting technologies and technical means in the cultivation of crops is investigated. The available methods and software tools used to solve these problems are studied. The expediency of developing a web-oriented software complex of the automated choice of agricultural technologies and MTF (machine and tractor fleet), providing accounting and operational processing of a variety of information describing objectively existing large number of factors, conditions and characteristics of production in the agricultural enterprise is substantiated. Based on the analysis of the main scientific and methodological components of cereal crop cultivation technologies, a structural scheme of a web-application is formed. Algorithms for software modules as components of the software package, with a common database and a unified common interface are developed. The implementation of the software package in the future will automate the process of forming an annual work planning, calculation of economic indicators, will allow the timely implementation of the necessary repair and maintenance activities to reduce power losses due to the inevitable deterioration of the technical condition of the ICE of the machine-tractor fleet in production conditions. The software package under development can be used in crop production in decision support systems based on digital technologies.

About the Authors

V. V. Alt
Russian Academy of Sciences
Russian Federation

Victor V. Alt, Doctor of Science in Engineering, Academician, Professor

Siberian Federal Scientific Centre of Agro-BioTechnologies

Novosibirsk Region

Krasnoobsk



O. V. Elkin
Russian Academy of Sciences
Russian Federation

Oleg V. Elkin, Candidate of Science in Engineering, Lead Researcher

630501

PO Box 463

Siberian Federal Scientific Centre of Agro-BioTechnologies

Novosibirsk Region

Krasnoobsk



S. P. Isakova
Russian Academy of Sciences
Russian Federation

Svetlana P. Isakova, Senior Researcher

Siberian Federal Scientific Centre of Agro-BioTechnologies

Novosibirsk Region

Krasnoobsk



O. F. Savchenko
Russian Academy of Sciences
Russian Federation

Oleg F. Savchenko, Candidate of Science in Engineering, Lead Researcher

Siberian Federal Scientific Centre of Agro-BioTechnologies

Novosibirsk Region

Krasnoobsk



References

1. Rose D. C., Wheeler R., Winter M., Lobley M., Chivers Ch.-A. Agriculture 4.0: Making it work for people, production, and the planet. Land Use Policy, 2021, vol. 100, no article 104933. DOI: 10.1016/j.landusepol.2020.104933. (In Russian).

2. Bashilov A. M., Korolev V. A. Digital transformation of agricultural enterprises. Vestnik agrarnoi nauki Dona = Don agrarian science bulletin, 2021, no. 3 (55), pp. 24–32. (In Russian).

3. Hovhannisyan T., Efendyan P., Vardanyan M. Creation of a digital model of fields with application of DJI phantom 3 drone and the opportunities of its utilization in agriculture. Annals of Agrarian Science, 2018, vol. 16, no. 2, pp. 177–180. DOI: 10.1016/j.aasci.2018.03.006.

4. Peter W. B. Phillips, Relf-Eckstein J.-A., Jobe G., Wixted B. Configuring the new digital landscape in western Canadian agriculture. Wageningen Journal of Life Sciences, 2019, vol. 90, pp. 1–11. DOI: 10.1016/j.njas.2019.04.001.

5. Talaviya T., Shah D., Patel N., Yagnik H., Shah M. Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 2020, vol. 4, pp. 58–73. DOI: 10.1016/j.aiia.2020.04.002.

6. Jha K., Doshi A., Patel P., Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2019, vol. 2, pp. 1–12. DOI: 10.1016/j.aiia.2019.05.004.

7. Gurfova S. A. Digitalization of agriculture: formation and development. Ekonomika i predprinimatel'stvo = Journal of Economy and entrepreneurship, 2020, no. 3 (116), pp. 445–448.

8. Zhumaxanova K. M., Yessymkhanova Z. K., Yessenzhigitova R. G., Kaydarova A. T. The current state of agriculture digitalization: problems and ways of solution. Central Asian Economic Review, 2019, no. 5 (128), pp. 144–155.

9. Rijswijk K., Klerkx L., Turner J. A. Digitalization in the New Zealand Agricultural Knowledge and Innovation System: Initial understandings and emerging organizational responses to digital agriculture. Wageningen Journal of Life Sciences. 2019, vol. 90–91, is. 1, pp. 1–14. DOI: 10.1016/j.njas.2019.100313.

10. Khondoker A. Perception and adoption of a new agricultural technology: Evidence from a developing country. Technology in society, 2018, vol. 55, pp. 126–135.

11. Yiyan Chen, Ye Li, Cunjin Li. Electronic agriculture, blockchain and digital agricultural democratization: Origin, theory and application. Journal of Cleaner Production, 2020, vol. 268. DOI: 10.1016/j.jclepro.2020.122071.

12. Truflyak E. V., Kurchenko N. Yu., Kreimer A. S. Monitoring and forecasting in the field of digital agriculture by the end of 2018. Krasnodar, KubGAU Publ., 2019, 100 p. (In Russian).

13. Izmailov A. Yu., Godzhaev Z. A., Grishin A. P., Grishin A. A., Dorokhov A. A. Digital agriculture (review of digital agricultural technologies). Innovatsii v sel'skom khozyaistve = Innovations in agriculture, 2019, no. 2 (31), pp. 41–52. (In Russian).

14. Elizarov V. P., Artyushin A. A., Tsench Yu. S. Perspective directions of development of national agricultural machinery. Vestnik VIESKh = VIESH Institute Herald, 2018, no. 2 (31), pp. 12–18. (In Russian).

15. Timonin S. B., Timonina A. S. The introduction of digital technologies in the processes of ensuring the optimal functioning of the machine-tractor unit. Niva Povolzh'ya = Volga Region Farmland, 2018, no. 3, pp. 124–132. (In Russian).

16. Startsev A. V., Alushkin T. E., Romanov S. V., Storozhev I. I. A model for determining the operating costs of machine-tractor units for sowing, taking into account the duration of work and the size of the area. Traktory i sel'khozmashiny = Tractors and agricultural machinery, 2020, no. 1, pp. 82–87. (In Russian). DOI: 10.31992/0321-4443-2020-1-82-87.

17. Chernoivanov V. I., Gabitov I. I., Negovora A. V. Digital technologies and electronic means in the system of technical maintenance and repair tractor and combine harvesters. Tekhnicheskij servis mashin = Machinery technical service, 2018, no. 130, pp. 74–81. (In Russian).

18. Didmanidze O. N., Dorokhov A. S., Kataev Yu. V. Trends in the development of digital technologies for diagnosing the technical condition of tractors. Tekhnika i oborudovanie dlya sela = Machinery and Equipment for Rural Area, 2020, no. 11 (281), pp. 39–43. (In Russian). URL: https://www.elibrary.ru/item.asp?id=44232635&ysclid=l8o8d59588260668758

19. Zubina V. A. Review and analysis of optimization methods and computer programs to improve the efficiency of MTF. Vestnik agrarnoi nauki Dona = Don agrarian science bulletin, 2018, no. 41, pp. 26–32. (In Russian).

20. Kokovikhin S. V., Bidnina I. A., Sharii V. A., Chervan' A. N., Drobit'ko A. V. Optimization of agro-technological process of cultivation of agricultural crops on irrigated lands with the use of information technology. Pochvovedenie i agrokhimiya = Soil Science and Agrochemistry, 2020, no. 2 (65), pp. 63–71. (In Russian).

21. Beilis V. M. Assessment of material and technical resources of crop production technologies. Sel'skokhozyaistvennye mashiny i tekhnologii = Agricultural machinery and technologies, 2017, no. 3, pp. 39–44. (In Russian). URL: https://www.vimsmit.com/jour/article/viewFile/194/150

22. Stepnykh N. V., Nesterova E. V., Zargaryan A. M. Economic evaluation of growing technologies of agricultural crops using a web application. Vestnik kurganskoi GSKhA = Bulletin of the Kurgan State Agricultural Academy, 2020, no. 1 (33), pp. 24–29. (In Russian).

23. Gostev A. V., Pykhtin A. I. Program for the rational choice of highly cost-effective adaptive technology of grain cultivation for various conditions of the European part of the Russian Federation. Journal of Applied Engineering Science, 2020, vol. 18, no. 679, pp. 216–221. DOI: 10.5937/jaes18-26312.

24. Tkachenko V. V. Methods of multicriterial comprehensive assessment and selection of the technology for growing crops. Nauchnyi zhurnal KubGAU = Scientific Journal of KubSAU, 2016, no. 123 (09), pp. 1–19. (In Russian).

25. Gümüscü A., Tenekeci M. E., Bilgili A. V. Estimation of wheat planting date using machine learning algorithms based on available climate data. Sustainable Computing: Informatics and Systems, 2020, vol. 28. DOI: 10.1016/j.suscom.2019.01.010.

26. Al't V. V., Balushkina E. A. Isakova S. P. Algorithm for choosing agrotechnologies and technical means in the production of crops. Sibirskii vestnik sel'skokhozyaistvennoi nauki = Siberian Herald of Agricultural Science, 2021, vol. 51, no. 4, pp. 93–100. (In Russian). DOI: 10.26898/0370-8799-2021-4-11.

27. Al't V. V., Balushkina E. A. Isakova S. P. Mathematical model for choosing grain crops cultivation technologies. Sibirskii vestnik sel'skokhozyaistvennoi nauki = Siberian Herald of Agricultural Science, 2020, vol. 50. no. 2, pp. 92–99. (In Russian). DOI: 10.26898/0370-8799-2020-2-11.

28. Yadrovskaya M. V. Revisiting computer modeling. Advanced Engineering Research, 2020, no. 20 (3). pp. 332–345.

29. Dobrolyubov I. P., Savchenko O. F., Ol'shevskii S. N. Principles to design a computer dynamic model of autotractor ICE. Vestnik NGAU = Bulletin of NSAU, 2014, no. 2, pp. 141–146. (In Russian).

30. Al't V. V., Savchenko O. F., Elkin O. V. Digital technology of assessment the power capacity of tractor fleet of an agricultural enterprise. Sel'skokhozyaistvennye mashiny i tekhnologii = Agricultural machinery and technologies, 2019, vol. 13, no. 4. pp. 25–31. (In Russian). DOI: 10.22314/2073-7599-2019-13-4-25-31.


Review

For citations:


Alt V.V., Elkin O.V., Isakova S.P., Savchenko O.F. Automated selection of agricultural technologies and tractor fleet of an agricultural enterprise: web-application structure and algorithms. Siberian Herald of Agricultural Science. 2022;52(4):107-119. (In Russ.) https://doi.org/10.26898/0370-8799-2022-4-12

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ISSN 0370-8799 (Print)
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