Intellectualization of technological systems in agriculture: Industry 4.0
https://doi.org/10.26897/2687-1149-2026-1-26-35
Abstract
The digital transformation of the agro-industrial sector within the «Industry 4.0» paradigm is impossible without the widespread adoption of artificial intelligence (AI) technologies. Despite the emergence of the «smart farming» approach and the «Agriculture 5.0» concept, the adoption of AI in agriculture remains fragmented due to methodological, technical, and organizational barriers. The study aims to review existing approaches to the intellectualization of agricultural machinery and equipment, evaluating their advantages, practical implementation, and scalability limitations. The authors conducted a bibliometric analysis and preformed terminological mapping of key concepts («automation», «digitalization», «intellectualization») across Russian and English scientific literature. Using a convergent cognitive-semantic approach, they have examined current intellectualization strategies in the agro-industrial sector. The review identified the main barriers to technology scaling, including the lack of a unified formalized approach to the implementation of intelligent systems; high initial investments, key interest rates, and tax burdens; the lack of unified data standards; the proprietary nature of software solutions from equipment manufacturers; equipment compatibility issues; import dependency; the underdevelopment of domestic IT solutions for the agro-industrial sector; and a shortage of specialists with interdisciplinary competencies. Overcoming these identified barriers will facilitate the successful digital transformation of the sector. A Gartner Hype Cycle model was developed to visualize the development stages of intellectualization technologies in the agro-industrial sector over the next 10 years, highlighting their strategic role in global food security. The study concludes that there is a need for local intellectualization of technological system elements based on agent-based modeling, along with supra-systemic solutions to integrate disparate elements into a unified control loop.
Keywords
About the Authors
I. V. PetukhovRussian Federation
Igor V. Petukhov, DSc (Eng), Professor
Lenina Sq., 3, Yoshkar-Ola, 424000, Mari El Republic
L. A. Steshina
Russian Federation
Liudmila A. Steshina, CSc (Eng); Associate Professor
Lenina Sq., 3, Yoshkar-Ola, 424000, Mari El Republic
I. S. Steshin
Russian Federation
Ilya S. Steshin, postgraduate student, Research Engineer
Lenina Sq., 3, Yoshkar-Ola, 424000, Mari El Republic
References
1. Steshina L.A., Petukhov I.V. Concept of human-oriented design of complex technological systems. Modern high technologies. 2023;7:92-96. (In Russ.) https://doi.org/10.17513/snt.39700
2. Din A., Ismail М., Shah B.et al. A deep reinforcement learning-based multi-agent area coverage control for smart agriculture. Computers and Electrical Engineering. 2022;101:108089. https://doi.org/10.1016/j.compeleceng.2022.108089
3. Fasciolo B., Panza L., Lombardi F. Exploring the integration of Industry 4.0 technologies in agriculture: A comprehensive bibliometric review. Sustainability. 2024;16(20):8948. https://doi.org/10.3390/su16208948
4. Bernhardt H., Bozkurt M., Brunschet R. et al. Challenges for agriculture through Industry 4.0. Agronomy. 2021;11(10):1935. https://doi.org/10.3390/agronomy11101935
5. Mishra S., Asha T., Rai C.K. Agriculture 5.0: Applications of artificial intelligence and internet of things. Indian Agriculture: Challenges, Priorities and Solutions. Springer, Singapore. 2025:283-297. https://doi.org/10.1007/978-981-96-5273-0_13
6. Arora C., Kamat A, Shanker S, Barve A. Integrating agriculture and industry 4.0 under “agri-food 4.0” to analyze suitable technologies to overcome agronomical barriers. British Food Journal. 2022;124(7):2061-2095. https://doi.org/10.1108/BFJ-08-2021-0934
7. Pogonyshev V.A., Pogonysheva D.A., Ulyanova N.D. Operating issues of agricultural machinery in the conditions of intellectualization of the agro-industrial complex. Vestnik Bryanskoy GSKhA. 2024;6:54-59. (In Russ.)
8. Murzaev E.A., Shablykin I.N. Scientific principles for creating intellectualized systems to monitor and control technological processes in organic production. AgroEkoInzheneriya. 2024;4:86-100. (In Russ.)
9. Chupina I.P., Fateeva N.B., Petrova L.N. Processes of development of automation and informatization in agriculture of the country. Agrarnoe obrazovanie i nauka. 2019;3:21. (In Russ.)
10. Misra N.N., Dixit Y., Al-Mallahi A. et al. IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal. 2022;9(9):6305-6324. https://doi.org/10.1109/JIOT.2020.2998584
11. Shafik W. Barriers to implementing computational intelligence-based agriculture system. Computational Intelligence in Internet of Agricultural Things. Cham, Springer. 2024;193-219. https://doi.org/10.1007/978-3-031-67450-1_8
12. Benos L., Moysiadis V., Kateris D. et al. Human–robot interaction in agriculture: A systematic review. Sensors. 2023;23(15):6776. https://doi.org/10.3390/s23156776
13. Rehman A., Saba T., Kashif M. et al. A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy. 2022;12(1):127. https://doi.org/10.3390/agronomy12010127
14. Kumar A. et al. Smart Crop Selection: Harnessing Machine Learning for Sustainable Agriculture in the Era of Industry 5.0. In: A. Chakir, R. Bansal, M. Azzouazi (eds) Industry 5.0 and Emerging Technologies. Studies in Systems, Decision and Control. 2024;565:111-134. https://doi.org/10.1007/978-3-031-70996-8_6
15. Concepcion R., Ramirez T.J., Alejandrino J. et al. A look at the near future: Industry 5.0 boosts the potential of sustainable space agriculture. 2022 IEEE14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2022:1-6. https://doi.org/10.1109/HNICEM57413.2022.10109559
16. Ragazou K. et al. Agriculture 5.0: A new strategic management mode for a cut cost and an energy efficient agriculture sector. Energies. 2022;15(9):3113. https://doi.org/10.3390/en15093113
17. Protosovitskii I.V., Zabello E.P., Prishchepov M.A., Daineko V.A. Ensuring the reliability and efficiency of the power industry in the agricultural sector of the Republic of Belarus in modern conditions. Energetika. proceedings of CIS higher education institutions and power engineering associations. 2020;63(2):116-128. (In Russ.) https://doi.org/10.21122/1029-7448-2020-63-2-116-128
18. Preite L., Vignali G. Artificial intelligence to optimize water consumption in agriculture: A predictive algorithm-based irrigation management system. Computers and Electronics in Agriculture. 2024;223;109126. https://doi.org/10.1016/j.compag.2024.109126
19. Belozerov V.V. i dr. Nanotechnology of “intellectualization” of energy accounting and suppression of fire-energy harm in engineering systems of residential buildings. Part 2. Nanotechnology in construction: Scientific Internet magazine. 2021;13(3):171-180. https://doi.org/10.15828/2075-8545-2021-13-3-171-180
20. Ganeshkumar C. et al. Artificial intelligence in agricultural value chain: review and future directions. Journal of Agribusiness in Developing and Emerging Economies. 2023;13(3):379-398. https://doi.org/10.1108/JADEE-07-2020-0140
21. Golubev I.G. Digitalization and use of artificial intelligence technologies in technical modernization of the agro-industrial complex. E3S Web of Conferences. EDP Sciences. 2021;291:04006. https://doi.org/10.1051/e3sconf/202129104006
22. Xu J., Gu B., Tian G. Review of agricultural IoT technology. Artificial Intelligence in Agriculture. 2022;6:10-22. https://doi.org/10.1016/j.aiia.2022.01.001
23. Raihan A. A systematic review of geographic information systems (GIS) in agriculture for evidence-based decision making and sustainability. Global Sustainability Research. 2024;3(1):1-24. https://doi.org/10.56556/gssr.v3i1.636
24. Islam R., Habibullah H., Hossain T. AGRI-SLAM: a real-time stereo visual SLAM for agricultural environment. Autonomous Robots. 2023;47(6):649-668. https://doi.org/10.1007/s10514-023-10110-y
25. Cerro J., Ulloa С., Barrientos А., Rivas J. Unmanned aerial vehicles in agriculture: A survey. Agronomy. 2021;11(2):203. https://doi.org/10.3390/agronomy11020203
26. Toscano F. et al. Unmanned aerial vehicle for precision agriculture: A review. IEEE Access. 2024;12:69188-69205. https://doi.org/10.1109/ACCESS.2024.3401018
27. Steshina L., Petukhov I., Glazyrin A. et al. An intelligent virtual environment for training with dynamic parameters. VSIP’20: Proceedings of the 2nd International Conference on Video, Signal and Image Processing. 2020;79-84. https://doi.org/10.1145/3442705.3442718
28. Zhang H., Wu L., Wang J. Research on innovative models of industry-education integration to promote sustainable education in agricultural machinery majors. Sustainable Futures. 2025;10:101072. https://doi.org/10.1016/j.sftr.2025.101072
29. Baychorova D.N., Selina E.V., Lytnev N.N. Economic efficiency of precision farming using high-tech agricultural machinery. Journal of Applied Research. 2024;12:58-64. (In Russ.)
30. Titirmare S., Margal P., Gupta S., Kumar D. AI-powered predictive analytics for crop yield optimization. Agriculture 4.0. CRC Press, 2024. pp. 89-110. https://doi.org/10.1201/9781003570219-5
31. Salem D.A., Hassan N.A., Hamdy R.M. Impact of transfer learning compared to convolutional neural networks on fruit detection. Journal of Intelligent & Fuzzy Systems. 2024;46(4):7791-7803. https://doi.org/10.3233/JIFS-233514
32. Chausov D.N., Petukhov I.V., Belyaev V.V. et al. Hardware-software complex for evaluation of operator’s activity. Bulletin of Moscow Region State University. Series: Physics-Mathematics. 2014;2:80-86.
33. Chicaiza K., Paredes R.X., Sarzosa I.M. et al. Smart farming technologies: A methodological overview and analysis. IEEE Access. 2024;12:164922-164941. https://doi.org/10.1109/ACCESS.2024.3487497
34. Nurimbetov R. et al. Multi-level diagnostics of agrarian economy subjects according to the degree of readiness for digital transformations. Conference Series: Earth and Environmental Science. IOP Publishing, 2022;1043(1):012006. https://doi.org/10.1088/1755-1315/1043/1/012006
35. Schönberger M. Artificial intelligence for small and medium-sized enterprises: identifying key applications and challenges. Journal of Business Management. 2023;21:89-112. https://doi.org/10.32025/JBM23004
36. Kalickaya V.V., Rykalina O.A., Vilacheva M.N. et al. Indexation of the disposal fees and import quotas mechanisms: macroeconomic effects and impact on the agricultural machinery market. Mezhdunarodnyi sel’skohozyajstvennyi zhurnal. 2025;5:629-634. (In Russ.)
37. Kalichkin V.K., Maksimovich K.Yu., Aleshchenko O.A., Aleshchenko V.V. Crop yield prediction: data structure and AI-powered methods. Agricultural Machinery and Technologies. 2025;19(2):33-44. (In Russ.) https://doi.org/10.22314/2073-7599-2025-19-2-33-44
38. Mizik T. How can precision farming work on a small scale? A systematic literature review. Precision agriculture. 2023;24(1):384-406. https://doi.org/10.1007/s11119-022-09934-y
39. Kheifets B.A., Chernova V.Yu. Adaptation of agriculture to new geopolitical conditions. Problemy prognozirovaniya. 2024;5:165-175. (In Russ.) https://doi.org/10.47711/0868-6351-206-165-175
40. Vodyannikov V.T., Eder A.V. Assessment and prospects for the digitalization of the agricultural sector of the Russian Economy. Agricultural Engineering (Moscow). 2024;26(2):49-56. (In Russ.) https://doi.org/10.26897/2687-1149-2024-2-49-56
41. Neftissov A., Biloshchytskyi A., Toxanov S., Ordabayev S. Mathematical, software and hardware support of the conceptual model of the information system of precision agriculture. Scientific Journal of Astana IT University. 2023;15(15):55-70. https://doi.org/10.37943/15TKFW1223
42. Savandha S.D., Fatimah А.F. Santika R. et al. The role of AI in enhancing agricultural labor efficiency: Perspectives from HR managers in agri-tech firms. Digital Agriculture and Innovation Journal. 2025;1(1):1-10. https://doi.org/10.59261/journaldaij.v1i1.1
43. Dara R., Hazrati Fard S.M., Kaur J. Recommendations for ethical and responsible use of artificial intelligence in digital agriculture. Frontiers in Artificial Intelligence. 2022;5:884192. https://doi.org/10.3389/frai.2022.884192
44. Uddin M., Chowdhury A., Kabir M.A. Legal and ethical aspects of deploying artificial intelligence in climate-smart agriculture. AI &Society. 2024;39(1):221-234. https://doi.org/10.1007/s00146-022-01421-2
45. Azzahra A., Prahitaningtyas S., Afridi F.et al. Recruitment and retention of tech-savvy talent for AI in agriculture: Challenges faced by HR in rural agribusiness. Digital Agriculture and Innovation Journal. 2025;1(1):37-47. https://doi.org/10.59261/journaldaij.v1i1.5
46. Shelkovnikov S.A., Kuznetsova I.G. Conceptual and methodological foundations for forming human capital under conditions of transition to digital agriculture. RUDN Journal of: Economics. 2022;30(1):110-123. (In Russ.) https://doi.org/10.22363/2313-2329-2022-30-1-110-123
Review
For citations:
Petukhov I.V., Steshina L.A., Steshin I.S. Intellectualization of technological systems in agriculture: Industry 4.0. Agricultural Engineering (Moscow). 2026;28(1):26-35. (In Russ.) https://doi.org/10.26897/2687-1149-2026-1-26-35
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