{"id":4917,"date":"2025-11-16T13:57:34","date_gmt":"2025-11-16T13:57:34","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=4917"},"modified":"2026-04-13T07:55:11","modified_gmt":"2026-04-13T06:55:11","slug":"27-5-2-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-5-2-en\/","title":{"rendered":"27.5.2 En"},"content":{"rendered":"\n

Multi-agent modeling in plant biology<\/strong><\/h1>\n\n\n\n

M.I. Anchekov, Zh.Kh. Kurashev<\/strong><\/p>\n\n\n\n

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Upload the full text<\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong><\/strong>: <\/em>Traditional methods, such as systems of algebraic or differential equations, L-systems, or functional-structural models, are often unable to fully simulate the dynamic interactions of plants with their environment. Multi-agent systems allow the modeled object to be represented as a collective of autonomous agents representing individual functional parts, each of which follows local rules that ensure decision-making and interaction with the external environment.
Aim<\/strong>. The study is to analyze modern approaches to multi-agent modeling in plant biology.
An analysis of several publications revealed that multi-agent modeling reproduces orange tree growth, root system architecture, the morphological adaptation of black alder, and the behavioral plasticity of animals in plant ecosystems, enabling the implementation of digital twins of wheat. The reviewed studies place particular emphasis on the emergent properties of the proposed models, which manifest themselves without explicitly defining global rules. The results of the analysis demonstrate the high potential of the multi-agent approach as a tool for modeling the morphological and physiological processes of biological systems, as well as its potential for digital farming, breeding, and yield forecasting in a changing climate. This approach is capable of accounting for spatial heterogeneity of the environment and temporal changes in conditions.
The presented review of research shows that the approach based on multi-agent systems is successfully applied to modeling tree growth, root systems, population dynamics, and digital twins of agricultural crops.<\/p>\n\n\n\n

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Keywords<\/strong><\/em><\/strong>:<\/em> multi-agent modeling, phenotypic plasticity, digital twin, agent-based modeling, emergent properties<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Anchekov M.I., Kurashev Zh.Kh. Multi-agent modeling in plant biology. News of the KabardinoBalkarian Scientific Center of RAS<\/em>. 2025. Vol. 27. No. 5. Pp. 26\u201333. DOI: 10.35330\/1991-6639-2025-27-5-26-33<\/p>\n\n\n\n

R<\/strong>eferences<\/summary>\n
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  3. Iordansky N.N. Phenotypic plasticity of organisms and evolution. Russkiy ornitologicheskiy zhurnal [Russian Ornithological Journal]. 2024. Vol. 33. No. 2385. Pp. 294\u2013303. EDN: HTMMOX. (In Russian)<\/li>\n\n\n\n
  4. Eschenbach C. The effect of light acclimation of single leaves on whole tree growth and competition \u2013 an application of the tree growth model ALMIS. Annals of Forest Science. 2000. Vol. 57. No. 5. Pp. 599\u2013609. DOI: 10.1051\/forest:2000145<\/li>\n\n\n\n
  5. Mu\u00dfmann M., Hofstadler D.N., Mammen S. von. An Agent-based, Interactive Simulation Model of Root Growth. The 2024 Conference on Artificial Life. 2024. DOI: 10.1162\/isal_a_00718<\/li>\n\n\n\n
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  12. Tikhonov A.A., Golovatyi V.S. Plant development planning models for digital twin services for crops. Modern Science. 2022. No. 2-2. Pp. 278\u2013283. EDN: IYEJFS. (In Russian)<\/li>\n\n\n\n
  13. Grieves M. Digital twin: manufacturing excellence through virtual factory replication. White Paper. 2015.<\/li>\n\n\n\n
  14. Skobelev P., Mayorov I., Simonova E. et al. Development of digital twin of plant for adaptive calculation of development stage duration and forecasting crop yield in a cyber-physical system for managing precision farming. Studies in Systems, Decision and Control. Springer. 2021. Pp. 83\u201396. DOI: 10.1007\/978-3-030-67892-0_8<\/li>\n\n\n\n
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    Pp. 363\u2013369. DOI: 10.1109\/ISORC.2008.25<\/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
    Zhiraslan Kh. Kurashev<\/strong>, Head 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;
    kurashev-j@mail.ru, ORCID: https:\/\/orcid.org\/0000-0001-9442-6122, SPIN-code: 8549-2620<\/p>\n\n\n\n

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    Multi-agent modeling in plant biology M.I. Anchekov, Zh.Kh. Kurashev Upload the full text Abstract: Traditional methods, such as systems of algebraic or differential equations, L-systems, or functional-structural models, are often unable to fully simulate the dynamic interactions of plants with their environment. Multi-agent systems allow the modeled object to be represented as a collective of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-4917","page","type-page","status-publish","hentry"],"yoast_head":"\n27.5.2 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-5-2-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.5.2 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Multi-agent modeling in plant biology M.I. 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