Multi-agent modeling in plant biology

M.I. Anchekov, Zh.Kh. Kurashev

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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 autonomous agents representing individual functional parts, each of which follows local rules that ensure decision-making and interaction with the external environment.
Aim. 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.

Keywords: multi-agent modeling, phenotypic plasticity, digital twin, agent-based modeling, emergent properties

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

References

1. Qu H., Wang Yo., Cai Lg, Wang T. Orange tree simulation under heterogeneous environment using agent-based model ORASIM. Simulation Modelling Practice and Theory. 2012.
   Vol. 23. Pp. 19–35. DOI: 10.1016/j.simpat.2011.12.005
2. Reuter H., Jopp F., Hölker F., Eschenbach Ch.A. The ecological effect of phenotypic plasticity – Analyzing complex interaction networks (COIN) with agent-based models. Ecological Informatics. 2008. Vol. 3. No. 1. Pp. 35–45. DOI: 10.1016/j.ecoinf.2007.03.010
3. Iordansky N.N. Phenotypic plasticity of organisms and evolution. Russkiy ornitologicheskiy zhurnal [Russian Ornithological Journal]. 2024. Vol. 33. No. 2385. Pp. 294–303. EDN: HTMMOX. (In Russian)
4. Eschenbach C. The effect of light acclimation of single leaves on whole tree growth and competition – an application of the tree growth model ALMIS. Annals of Forest Science. 2000. Vol. 57. No. 5. Pp. 599–609. DOI: 10.1051/forest:2000145
5. Mußmann 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
6. Raies Y., von Mammen S. A Swarm Grammar-Based Approach to Virtual World Generation. Lecture Notes in Computer Science. 2021. Pp. 459–474. DOI: 10.1007/978-3-030-72914-1_30
7. Lindenmayer A. Mathematical models for cellular interactions in development II. Simple and branching filaments with two-sided inputs. Journal of Theoretical Biology. 1968. Vol. 18. No. 3. Pp. 300–315. DOI: 10.1016/0022-5193(68)90080-5
8. Prusinkiewicz P. Pillars of theoretical biology: Mathematical models for cellular interaction in development. I and II. Journal of Theoretical Biology. 2025. No. 609. P. 112142
9. Li X., Su Zh., Sun H., Zheng P. Agent-based plant growth modeling. ICICSE ’09: Proceedings of the 2009 Fourth International Conference on Internet Computing for Science and
   Engineering. 2009. Pp. 6–11. DOI: 10.1109/ICICSE.2009.8
10. Garro A., Falcone A., Baldoni M. et al. Intelligent agents: multi-agent systems. Encyclopedia of Bioinformatics and Computational Biology. 2025. Pp. 379–385.
11. Skobelev P., Laryukhin V., Simonova E. et al. Multi-agent approach for developing a digital twin of wheat. In 2020 IEEE International Conference on Smart Computing (SMARTCOMP). 2020. Pp. 268–273. DOI: 10.1109/smartcomp50058.2020.00062
12. Tikhonov A.A., Golovatyi V.S. Plant development planning models for digital twin services for crops. Modern Science. 2022. No. 2-2. Pp. 278–283. EDN: IYEJFS. (In Russian)
13. Grieves M. Digital twin: manufacturing excellence through virtual factory replication. White Paper. 2015.
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. 83–96. DOI: 10.1007/978-3-030-67892-0_8
15. Lee E.A. Cyber physical systems: design challenges. 2008 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC). 2008.
    Pp. 363–369. DOI: 10.1109/ISORC.2008.25

Information about the authors

Murat I. Anchekov, Head of the Laboratory of Simulation Modeling of Phenogenetic Processes of the Scientific and Innovation Center “Intelligent Genetic Systems”, 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, Head of the Scientific and Innovation Center “Intelligent Genetic Systems”, 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