{"id":6058,"date":"2026-01-21T12:06:04","date_gmt":"2026-01-21T12:06:04","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=6058"},"modified":"2026-06-02T10:31:59","modified_gmt":"2026-06-02T09:31:59","slug":"27-6-12-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-6-12-en\/","title":{"rendered":"27.6.12 En"},"content":{"rendered":"\n

Development of a software architecture for agent-based modeling of intelligent agricultural systems<\/strong><\/h1>\n\n\n\n

M.I. Anchekov<\/strong><\/p>\n\n\n\n

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Abstract<\/strong><\/strong>. <\/em>This article presents the architecture of an agent-based modeling software package for intelligent agricultural systems, focused on modeling the interactions between robots, plants, and infrastructure in an apple orchard. The system integrates physical, sensor, effector, energy, and computational models into a single discrete 3D environment and supports decentralized federated learning without a centralized server. Particular attention is paid to agent autonomy, asynchronous simulation execution, and the ability to integrate with real sensors and robots.
Aim<\/strong>. The study aims to develop the architecture of an agent-based modeling software package designed for simulating intelligent integrated information and control systems in a real, physically correct, dynamic, and partially observable environment.
Research methods<\/strong>. The primary research method is agent-based (multi-agent) modeling, which allows simulating the interaction of autonomous agents in an uncertain and dynamic environment. Object-oriented design using UML notation is used to structure the architecture and decompose tasks.
Results<\/strong>. A software architecture is proposed that takes into account entities such as a simulated World, Agent, Entity, Billboard, and Computer.
Conclusions<\/strong>. The proposed platform ensures the reproducibility of experiments, scalability, and serves as a basis for testing collective behavior algorithms in heterogeneous and resource-limited agricultural environments.<\/p>\n\n\n\n

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Keywords<\/strong><\/em><\/strong>:<\/em> precision agriculture, simulation modeling, collaborative robotics, federated learning<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Anchekov M.I. Development of a software architecture for agent-based modeling of intelligent agricultural systems. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2025. Vol. 27. No. 6. Pp. 135\u2013141. DOI: 10.35330\/1991-6639-2025-27-6-135-141<\/p>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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  1. Anchekov M.I., Leshkenov A.M. System for virtual modeling of robotic systems for agricultural purposes. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2023. No. 6(116). Pp. 125\u2013131. DOI: 10.35330\/1991-6639-2023-6-116-125-131. (In Russian)<\/li>\n\n\n\n
  2. Osorio-Antonia J., Rivas-Tovar L.A., Ju\u00e1rez-P\u00e9rez F. Simulation of corn production in
    chicontepec veracruz with agent-based modeling. Computaci\u00f3n y Sistemas. 2025. Vol. 29. No. 1.
    Pp. 529\u2013544. DOI: 10.13053\/CyS-29-1-5293<\/li>\n\n\n\n
  3. Luke S., Cioffi-Revilla C., Panait L. et al. Mason: a multiagent simulation environment. Simulation. 2005. Vol. 81. No. 7. Pp. 517\u2013527. DOI: 10.1177\/0037549705058073<\/li>\n\n\n\n
  4. Lopez-Jimenez J., Quijano N., Vande Wouwer A. An agent-based crop model framework for heterogeneous soils. Agronomy. 2021. Vol. 11. No. 1. P. 85. DOI: 10.3390\/agronomy11010085<\/li>\n\n\n\n
  5. North M.J., Collier N.T., Vos J.R. Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation (TOMACS). Vol. 16. No. 1. Pp. 1\u201325. DOI: 10.1145\/1122012.112201<\/li>\n\n\n\n
  6. Konkina V., Martynushkin A. Forecasting the size of the dairy market in AnyLogic environment. E3S Web of Conferences. EDP Sciences, 2021. Vol. 282. P. 01002. DOI: 10.1051\/e3sconf\/202128201002<\/li>\n\n\n\n
  7. Ltaief H., Karaduman B., Boussaid et al. Agent based implementation of a robot arm and smart production line using jade framework. 2022 11th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2022. Pp. 1\u201312. DOI: 10.1109\/MECO55406.2022.9797162<\/li>\n\n\n\n
  8. Yu X., Queralta J.P., Westerlund T. Towards lifelong federated learning in autonomous mobile robots with continuous sim-to-real transfer. Procedia Computer Science. 2022. Vol. 210. Pp. 86\u201393. DOI: 10.1016\/j.procs.2022.10.123<\/li>\n\n\n\n
  9. Na S., Rou\u010dek T., Ulrich J. et al. Federated reinforcement learning for collective navigation of robotic swarms. IEEE Transactions on cognitive and developmental systems. 2023. Vol. 15. No. 4. Pp. 2122\u20132131. DOI: 10.1109\/TCDS.2023.3239815<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong>s<\/summary>\n
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    Murat I. Anchekov<\/strong>, Head of the Laboratory of Simulation Modeling of Phenogenetic Processes of the Scientific and Innovation Center \u201cIntelligent Genetic Systems\u201d, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    2, Balkarov street, Nalchik, 360010, Russia;
    murat.antchok@gmail.com, ORCID: https:\/\/orcid.org\/0000-0002-8977-797X, SPIN-code: 3299-0927<\/p>\n\n\n\n

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    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Development of a software architecture for agent-based modeling of intelligent agricultural systems M.I. Anchekov Abstract. This article presents the architecture of an agent-based modeling software package for intelligent agricultural systems, focused on modeling the interactions between robots, plants, and infrastructure in an apple orchard. 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Anchekov Abstract. This article presents the architecture of an agent-based modeling software package for intelligent agricultural systems, focused on modeling the interactions between robots, plants, and infrastructure in an apple orchard. 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