ISSN 2409-7616

Kudasheva M.S.


UDC 338.43


Kudasheva M.S.1 (Penza, Russian Federation) –

1Penza State Technological University

Abstract. The digital economy is a promising direction for the development and transformation of economic processes in all areas of business. End—to-end technologies are business digitalization tools within the framework of the “Industry 4.0” concept, which include big data; neurotechnologies and artificial intelligence; the Internet of Things, distributed registry systems. They allow you to increase business efficiency regardless of the scope of the enterprise. Agriculture is one of the leading branches of ensuring the economic security of any state. Within the framework of the agro-industrial complex of Russia, small forms of management make a significant contribution, which is confirmed by a high proportion of the rural population and significant volumes of agricultural production in private households and peasant (farmer) farms. At the same time, the availability of end-to-end technologies and the overall digitalization of small agribusiness is at a very low level. The article analyzes the level of digitalization of Russian agribusiness, identifies the features of the functioning of small forms of farming in rural areas. The analysis of the information and communication infrastructure of agriculture indicates the presence of such barriers to digitalization as financial or territorial difficulties, insufficient skills to work on the Internet and the lack of technical connectivity to it. As part of the study, a ranking of end-to-end technologies by the difficulty of implementation for small agribusiness was carried out. The description and barriers to the introduction of the following technologies are given: “Big Data”, “Neural networks and artificial Intelligence”, “Industrial Internet of Things”, “Robotics and Sensors”. The factors used for ranking are price, the need for IT infrastructure, requirements for the qualification of employees, the percentage of farms using this end-to-end technology.

Keywords: agriculture, end-to-end technologies, digitalization, small agribusiness, big data, Internet of things, neurotechnology, robotics.


  1. Andryushechkina N.A., Musikhina L.V. Internet of things in agriculture. Scientific and Technical Bulletin: Technical systems in agriculture, 2020, no. 1 (6), pp. 42-47. (In Russian). URL:
  2. Afanasyeva M.S., Karmyshova Yu.V. Life cycle research of non-profit agricultural organizations. Successes of modern science and education, 2016, vol. 3, no. 11, pp. 85-90. (In Russian). URL:
  3. Varich M. I., Davletshin R. R. Digitalization of agriculture within the framework of the agricultural development project in the Russian Federation until 2025.  Young Scientist, 2020, no. 2 (292), pp. 354-357. (In Russian). URL:  
  4. Gorbunova O.S., Petryakova S.V., Pilnikova I.F., Krokhalev A.A. Agricultural machinery and the concept of the Internet of Things. Education and Law, 2019, no. 8, pp. 222-228. (In Russian) URL:  
  5. Dadalko V.A., Zhelyabin D.V. Application of end-to-end technologies of digital economy in agriculture. State power and local self-government, 2021, no. 10, pp. 31-36. (In Russian). EDN: DBYMTF, DOI: 10.18572/1813-1247-2021-10-31-36
  6. Dementiev V.E. Technological development and structural changes in the economy. AlterEconomics, 2022, vol. 19, no. 1, pp. 116-130. (In Russian). EDN: TCCLCE, DOI: 10.31063/AlterEconomics/2022.19-1.7
  7. Demichev V.V. The impact of big data on the development of agriculture in Russia. Russian Economic online magazine, 2020, no. 3, p. 10. (In Russian) URL:
  8. Zayats O.A., Nazarova Yu.N., Strizhakova E.A., Penkova R.I. BIG DATA technologies in agriculture. Fundamental research, 2022, no. 7, pp. 35-40. (In Russian). URL:
  9. Istomina N.L. End-to-end technologies: changing the structure of traditional industry. Photonics, 2020, vol. 14, no. 6, pp. 520-523. (In Russian). EDN: HEHAKI DOI: 10.22184/1993-7296.FRos.2020.14.6.520.523
  10. Medennikov V.I. Digital technologies for the national platform “Digital agriculture”. Chronoeconomics, 2020, no. 5 (26), pp. 12-17. (In Russian). URL:  
  11. Mikenin D.V., Minin Yu.V. Advantages of using software based on neural networks in agriculture. Science and Education, 2019, vol. 2, no. 4, p. 240. (In Russian). URL:
  12. Morozov N.M., Khusainov I.I., Varfolomeev A.S. Efficiency of application of robotic systems in animal husbandry. Bulletin of the All-Russian Scientific Research Institute of mechanization of animal husbandry, 2019, no. 1 (33), pp. 57-62. (In Russian). URL:  
  13. Plaksin I.E., Trifanov A.V., Plaksin S.I. Analysis of the use of automated and robotic complexes in agriculture. Technologies and technical means of mechanized production of crop production and animal husbandry, 2018, no. 97, pp. 73-83. (In Russian). EDN: VNRJRC, DOI: 10.24411/0131-5226-2018-10091
  14. Pogonyshev V.A., Pogonysheva D.A., Torikov V.E. Neural networks in digital agriculture. Bulletin of the Federal State Educational Institution of the Bryansk State Agricultural Academy, 2021, no. 5 (87). EDN: MDJBSL, DOI: 10.52691/2500-2651-2021-87-5-68-71 
  15. Pogrebnaya N.V., Barysheva D.N., Lamazyan L.S. Digital transformation in agriculture: problems and prospects. Bulletin of the Altai Academy of Economics and Law, 2022, no. 9-1, pp. 118-123. (In Russian). EDN: JQXZJS, DOI: 10.17513/vaael.2401
  16. Skvortsov E.A., Skvortsova E.G., Sandu I.S. Transition of agriculture to digital, intelligent and robotic technologies. The economy of the region, 2018, vol. 14, no. 3, pp. 1014-1028. (In Russian). EDN: XYYCDR, DOI: 10.17059/2018-3-23
  17. Smirnov A.V. Digital society: theoretical model and Russian reality. Monitoring of public opinion: economic and social changes, 2021, no. 1 (161), pp. 129-153. (In Russian). EDN: SZLWQF, DOI: 10.14515/monitoring.2021.1.1790
  18. Asseng S., Palm C., Anderson J. Implications of new technologies for future food supply systems. The Journal of Agricultural Science, 2021, vol. 159(5-6), pp. 315-319. DOI:
  19. Benyam A., Soma T., Fraser E. Digital agricultural technologies for food loss and waste prevention and reduction: Global trends, adoption opportunities and barriers. Journal of Cleaner Production, 2021, vol. 323, Id 129099. DOI:
  20. Duncan E., Rotz S., Magnan A. Disciplining land through data: The role of agricultural technologies in farmland assetisation. Sociologia Ruralisthis link is disabled, 2022, vol. 62(2),  pp. 231–249. DOI:
  21. McCampbell M., Adewopo J., Klerkx L. Are farmers ready to use phone-based digital tools for agronomic advice? Ex-ante user readiness assessment using the case of Rwandan banana farmers. Journal of Agricultural Education and Extensionthis link is disabled, 2023, vol. 29(1), pp. 29–51. DOI:
  22. Wolfert S., Verdouw C., van Wassenaer L. Digital innovation ecosystems in agri-food: design principles and organizational framework. Agricultural Systemsthis, 2023, vol. 204 (2), Id 103558. DOI:

For citation: Kudasheva M.S. End-to-end digital technologies for small agribusiness. CITISE, 2023, no. 2, pp. 112-124. DOI: