Use of artificial intelligence for managing a livestock farm

The implementation of intelligent digital control systems is essential for achieving high performance in agricultural enterprises. The study applies the biomachine systems theory to livestock farm management to analyze the functional links between complex system components. The research justifies the structure and functionality of an artificial intelligence (AI) framework for managing livestock biomachine systems. In this model, the farm is represented as an undirected multigraph, where vertices represent the «Human-Machine-Animal-Product-Environment» (H-M-A-P-E) elements and edges represent their functional interconnections. The study examines localized biotechnological systems – specifically milking and primary processing, feed preparation and distribution, microclimate control, and manure removal – analyzing their functional relationships through a directed multigraph. The authors categorize AI functions within these localized systems and provide a structural-functional diagram illustrating the interaction between milking and feeding systems within the Internet of Things (IoT) framework. The direct exchange of signals between these local systems, independent of a centralized «control center» (workstation), enables autonomous functioning and effective coordinated management. The systematization of AI functions presented here facilitates the development of intelligent telecommunication systems for monitoring operators’ performance, machine efficiency, physiological condition of animals, and the overall economic and environmental sustainability of the enterprise.

1. Lobachevskiy Ya.P., Lachuga Yu.F., Izmaylov A.Yu., Shogenov Yu.Kh. Scientific and technical achievements of agricultural engineering organizations in the context of digital transformation of agriculture. Machinery and Equipment for Rural Area. 2023;4(310):2-5. (In Russ.)

2. Lobachevskiy Ya.P., Dorokhov A.S. Digital technologies and robotic devices in the agriculture. Agricultural Machinery and Technologies. 2021;15(4):6-10. (In Russ.) https://doi.org/10.22314/2073-7599-2021-15-4-6-10

3. Zatsarinny A.A., Medennikov V.I., Raikov A.N. Integration of agricultural artificial intelligence applications into a single digital platform. Information Society. 2023;1:127-138. (In Russ.)

4. Yalunina E.N., Pryadilina N.K., Skvorcov E.A. Improving the process of making management decisions in agriculture using artificial intelligence systems. Agrarian Bulletin of the Urals. 2024;24(3):440-449. (In Russ.) https://doi.org/10.32417/1997-4868-2024-24-03-440-449

5. Iksanov R.A., Vladimirov I.A., Gizzatullin R.Kh. The impact of implementing artificial intelligence technologies on resource saving in agriculture. Bulletin of KSAU. 2025;3:131-139. (In Russ.) https://doi.org/10.36718/1819-4036-2025-3-131-139

6. Chernoivanov V.I., Tolokonnikov G.K. Agrocyborg as a biomachine system. Machinery and Equipment for Rural Area. 2022;9(303):2-5. (In Russ.)

7. Skvorcov E.A., Nabokov V.I., Nekrasov K.V et al. Application of technologies of artificial intelligence in agriculture. Agrarian Bulletin of the Urals. 2019;8:91-98. (In Russ.) https://doi.org/10.32417/article_5d908ed78f7fc7.89378141

8. Kirsanov V.V., Tsoi Yu.A. Trends in the development of biotechnical systems in animal husbandry. Agricultural Machinery and Technologies. 2020;14(3):27-32. (In Russ.) https://doi.org/10.22314/2073-7599-2020-14-3-27-32

9. Jha K., Doshi A., Patel P., Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2019;2:1-12. https://doi.org/10.1016/j.aiia.2019.05.004

10. Budzko V.I., Medennikov V.I. System analysis of educational digital ecosystems in the agro-industrial complex. Highly Available Systems. 2023;19(1):46-58. (In Russ.)

11. Pavlenko E.Y. Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning. Modeling and Analysis of Information Systems. 2023;30(4):288-307. (In Russ.) https://doi.org/10.18255/1818-1015-2023-4-288-307

12. Solovov A.V., Menshikova A.A. Cognitive modeling of adaptive learning processes. Ontology of Designing. 2024;14(2):181-195. https://doi.org/10.18287/2223-9537-2024-14-2-181-195

13. Kirsanov V.V., Dorokhov A.S., Ivanov Yu.A. Graph analytics of the performance of local biotechnical systems in animal husbandry. Agricultural Engineering (Moscow). 2023;25(2):4-9. (In Russ.). https://doi.org/10.26897/2687-1149-2023-2-4-9

14. Raevskaya E.G. Introducing artificial intelligence in Chinese agriculture (review). Agricultural Science Euro-North-East. 2024;25(5):739-753. (In Russ.) https://doi.org/10.30766/2072-9081.2024.25.5.739-753

15. Gill S.S., Goel S., Macovei A. et al. The agritech revolution: Artificial intelligence reshaping the agriculture. Current Plant Biology. 2025;44:100554. https://doi.org/10.1016/j.cpb.2025.100554

16. Kirsanov V.V., Pavkin D.Yu., Vladimirov F.E. et al. Monitoring and control of the “Animal” subsystem in the complex biotechnical system “Man-Machine-Animal” of a dairy farm. Agricultural Engineering (Moscow). 2020;(6):4-10. (In Russ.) https://doi.org/10.26897/2687-1149-2020-6-4-10

17. Dubrovsky D.I. The task of the creation of artificial general intelligence and the problem of consciousness. Russian Journal of Philosophical Sciences. 2021;64(1):13-44. (In Russ.) https://doi.org/10.30727/0235-1188-2021-64-1-13-44

18. Ezanno P., Picault S., Beaunée G. et al. Research perspectives on animal health in the era of artificial intelligence. Veterinary Research. 2021;52:40. https://doi.org/10.1186/s13567-021-00902-4

19. Han Z., Cheng L., Tian L., Xing L. A graph theory-based optimization design for complex manufacturing processes. IEEE Access. 2020;8:95547-95558. https://doi.org/10.1109/ACCESS.2020.2991218