Subject. Modernization of agricultural production, improving the level of food security in the Russian Federation. Objectives. Analysis of opportunities for applying machine learning methods in the bioeconomy. Methods. General scientific research methods were applied. Results. The main problem hindering the use of artificial intelligence in agriculture is the lack of an information base. The introduction of artificial intelligence into the industry will not only increase production efficiency but also ensure the availability of quality food products. Conclusions. The research results may be taken into account by the Ministry of Agriculture of the Russian Federation when monitoring threats to the country's food security.
Li R., Li L., Xu Y., Yang J. Machine Learning Meets Omics: Applications and Perspectives. Brief Bioinform, 2022, vol. 23, iss. 1. DOI: 10.1093/bib/bbab560
Ruan L., Dias M.P.I., Wong E. Deep Neural Network Supervised Bandwidth Allocation Decisions for Low-Latency Heterogeneous E-Health Networks. Journal of Lightwave Technology, 2019, vol. 37, iss. 16, pp. 4147–4154. URL: Link
Jagannath J., Polosky N., Jagannath A. et al. Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey. Ad Hoc Networks, 2019, vol. 93. DOI: 10.1016/j.adhoc.2019.101913
Stern M., Murugan A. Learning Without Neurons in Physical Systems. Annual Review of Condensed Matter Physics, 2023, vol. 14, iss. 1, pp. 417–441. DOI:10.1146/annurev-conmatphys-040821-113439
Orlova E.V. [Reinforcement learning as an artificial intelligence technology to solve socio-economic problems: algorithms performance assessment]. π-Economy, 2023, vol. 16, iss. 5, pp. 38–50. (In Russ.) EDN: OHKJKP
Sutton R.S., Barto A.G. Reinforcement Learning: An Introduction. MIT Press, 1998, 342 p.
Chio C., Freeman D. Machine Learning and Security Protecting Systems with Data and Algorithms. O'Reilly Media, 2018, 386 p.
Kharyuk P., Nazarenko D., Oseledets I. et al. Employing Fingerprinting of Medicinal Plants by Means of LC-MS and Machine Learning for Species Identification Task. Scientific Reports, 2018, vol. 8, iss. 1. DOI: 10.1038/s41598-018-35399-z
Abdul Razak S.F., Yogarayan S., Sayeed M.S., Mohd Derafi M.I.F. Agriculture 5.0 and Explainable AI for Smart Agriculture: A Scoping Review. Emerging Science Journal, 2024, vol. 8, iss. 2, pp. 744–760. DOI: 10.28991/ESJ-2024-08-02-024
Koroleva L.A., Fan I. [Global experience in improving food security through the integration of blockchain and IoT technologies in food supply chain management]. Rossiya v global'nom mire, 2025, vol. 28, iss. 3, pp. 32–48. (In Russ.) EDN: SYGYIR
Korzhevich V.V., Bodnar А.V. [Review of the effectiveness of blockchain technology application in logistics and supply chain management]. Informatika i kibernetika, 2024, no. 2, pp. 23–28. (In Russ.) EDN: QYWTCA
Bodnar А.V., Pokhlebin P.S., Nesterenko А.R. [Investments in artificial intelligence: assessment of economic feasibility and potential risks]. Informatika i kibernetika, 2024, no. 3, pp. 25–31. (In Russ.) EDN: DDDMHD
Panyukova V.V. [International experience of using blockchain technology in supply chain management]. Nalogi. Pravo, 2018, vol. 11, iss. 4, pp. 60–67. (In Russ.) EDN: XWIEHJ
Trufanov S.A. [The future of management, marketing and production in the context of information technology development and evolution of generations]. Upravlenie. Finansy, 2017, no. 3, pp. 45–56. (In Russ.) EDN: ZGWGHX
Lugachev M.I., Kudrin A.A. (eds). Issledovaniya po tsifrovoi ekonomike: monografiya [Research on the digital economy: a monograph]. Moscow, Lomonosov Moscow State University Publ., 2025, 304 p.
