Implementation of automated crop rotation system design on a digital platform for agro-industrial complex management

The objective of this research is to formalize the growing integration of data and management algorithms in agriculture at the advanced stage of the digital economy (DE). This has resulted in a promising innovative project for a digital management platform (DMP) for the production economy, integrating all agricultural management tasks ontologically and functionally with a unified, scientifically based approach to their design. A key element of this approach should be a universal computer-aided design (CAD) system for crop rotation (CR) structures, substantiated by mathematical modeling. The priority for developing such a CAD system in the DE is dictated by the nature and ability of scientifically based crop rotations to determine all techno-logical processes in agriculture. The DMP project, in turn, was the result of a synthesis and subsequent formalization of several key principles of digital economy that emerged during the spontaneous attempts to integrate information resources and infor-mation management systems (IMS) by large organizations in the process of computerizing their businesses, as well as the experience of implementing IMS subsystems at over 1,000 enterprises as part of the "Electronization of Agriculture" program. Thus, working with experts from the Kuban Research Institute of Agriculture, which has extensive experimental data for many years, a mathematical model for the formation of optimal CR was developed. This research is supported by global trends toward the emergence of CAD systems replacing traditional manual system design, a consequence of the development of both integra-tion technologies and the simultaneous improvement of mathematical modeling methods. This paper utilizes the mathematical apparatus of graph theory in the form of a container packaging problem while simultaneously searching for the shortest path, as well as an optimization mathematical model of the production and logistics chain for both growing and further movement of produce within and outside the farm, which is especially important for agricultural holdings. In the future, with the transition of the agro-industrial complex to a DMP with the accumulation of a sufficient volume of data, it will be possible to set the task of forming new CR that take into account the heterogeneity of fields, using artificial intelligence.

1. Chernov I., Dranko O., Guo М., Novikov D., Pan J., Raikov A. Scenario Modelling of Country's Region Development with Artificial Intelligence Support. 12th IEEE International Conference on Communication Systems and Network Technologies, CSNT. Bhopal, India, 2023. pp. 653–660. DOI: https://doi.org/10.1109/CSNT57126.2023.10134656

2. Borovkov A. I., Ryabov Yu. A., Kukushkin K. V., Maruseva V. M., Kulemin V. Yu. Digital twins and the digital transformation of defense industry enterprises. Vestnik Vostochno-Sibirskoy otkrytoy akademii. 2019;(32):2. (In Russ.). URL: https://elibrary.ru/item.asp?id=37180048

3. Grieves M. Digital Twin Certified: Employing Virtual Testing of Digital Twins in Manufacturing to Ensure Quality Products. Machines. 2023;11(8):808. DOI: https://doi.org/10.20944/preprints202305.1758.v1

4. Budzko V., Medennikov V., Keyer P. System Analysis of Subject Identification of Digital Twin in Agriculture. In: Samsonovich A. V., Liu T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA. Studies in Computational Intelligence. Vol 1130. Springer, Cham, 2023. pp. 135–142. DOI: https://doi.org/10.1007/978-3-031-50381-8_16

5. Medennikov V., Raikov A. Formation of the digital platform for precision farming with mathematical modeling. CEUR Workshop Proceedings. 2020;2790:114. URL: https://elibrary.ru/item.asp?id=45075916

6. Vasiliev S. A. Intelligent technology for quality control of tillage in the precision farming system. Zemledelie. 2022;(3):36–41. (In Russ.). DOI: https://doi.org/10.24412/0044-3913-2022-3-36-41

7. Asadov Kh. G., Makhmudova V. Kh. Construction of a mathematical model of fertilizer application in agricultural fields in precision agriculture. Agrarnyy nauchnyy zhurnal = The Agrarian Scientific Journal. 2021;(11):8–11. (In Russ.). DOI: https://doi.org/10.28983/asj.y2021i11pp8-11

8. Sergeeva N. V. Economic justification of precision farming elements. Ekonomika sel'skogo khozyaystva Rossii = Economics of Agriculture of Russia. 2024;(5):2–7. (In Russ.). DOI: https://doi.org/10.32651/245-2

9. Mikhaylenko I. M., Timoshin V. N. Automated control complex of agrotechnology in precision farming. Vestnik rossiyskoy sel'skokhozyaystvennoy nauki = Vestnik of the Russian agricultural science. 2024;(5):54–62. (In Russ.). DOI: https://doi.org/10.31857/S2500208224050127

10. Medennikov V. I. Modeling of the formation of a digital platform for managing the Agroindustrial complex. Ekonomika sel'skogo khozyaystva Rossii = Economics of Agriculture of Russia. 2022;(7):83–90. (In Russ.). DOI: https://doi.org/10.32651/227-83

11. Zatsarinnyy A. A., Medennikov V. I., Raykov A. N. Integration of agricultural artificial intelligence applications into a single digital platform. Informatsionnoe obshchestvo = Information Society. 2023;(1):127–138. (In Russ.). DOI: https://doi.org/10.52605/16059921_2023_01_127

12. Khabarova I. A., Nepoklonov V. B. Land degradation of the Russian South. Izvestiya vysshikh uchebnykh zavedeniy. Geodeziya i aerofotos"emka = Izvestia Vuzov. Geodesy and Aerophotosurveying. 2017;(2):111–115. (In Russ.). URL: https://elibrary.ru/item.asp?id=29186002

13. Makarov O. A., Kuznetsov M. S., Demidov V. V., Karpova D. V., Shul'ga P. S., Abdulkhanova D. R., Esafova E. N., Kubarev E. N. Main areas of scientific research at the department of soil erosion and conservation: current state and prospects of development. Vestnik Moskovskogo universiteta. Seriya 17: Pochvovedenie = Moscow University Soil Science Bulletin. 2023;(4):70–82. (In Russ.). DOI: https://doi.org/10.55959/MSU0137-0944-17-2023-78-4-70-82

14. Khomyakov D. M. Soil – an essential component of the biosphere and the global food system (critical assessment of the situation). Vestnik Moskovskogo universiteta. Seriya 17: Pochvovedenie = Lomonosov Soil Science Journal. 2020;(4):3–16. (In Russ.). URL: https://elibrary.ru/item.asp?id=44097163

15. Medennikov V. I. The experience and implications of developing a cognitive map for the integrated digitalization of agriculture. Management of Largescale systems Development (MLSD'2025): Proceedings of the XVIIIth International conference. Moscow, September 24-26, 2025. Moscow: IPU im. V. A. Trapeznikova RAN, 2025. pp. 302–310. URL: https://elibrary.ru/item.asp?id=88809623

16. Medennikov V. I., Keyer P. A. Geographical reference of the formation of promising crope rotations based on a mathematical model of management of the production and logistics chain of plant products. Sistemy vysokoy dostupnosti = Journal Highly available systems. 2023;19(4):73–81. (In Russ.). DOI: https://doi.org/10.18127/j20729472-202304-06

17. Barashov E. B., Egorkin A. V., Lemtyuzhnikova D. V., Posypkin M. A. Efficiency of the reduction algorithms in the bin packing problem. Sistemy i sredstva informatiki = Systems and Means of Informatics. 2023;33(3):61–75. (In Russ.). DOI: https://doi.org/10.14357/08696527230305

18. Furems E. M. Approximate solution scheme for inverse binpacking problem subject to decision making preferences. Iskusstvennyy intellekt i prinyatie resheniy = Artificial Intelligence and Decision Making. 2018;(3):112–121. (In Russ.). DOI: https://doi.org/10.14357/20718594180321