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Draft:Functional-Structural Plant Models

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  • Comment: Much of the text lacks in-line citations. Ca talk to me! 11:52, 12 June 2025 (UTC)



Functional–structural plant models (FSP models, also FSPMs) are computational models that integrate a three-dimensional (3D) representation of plant architecture (structure) with physiological processes (function), such as light interception and photosynthesis, carbon allocation, and water relations.[1] They are used to study how plant form and physiological processes interact with environmental conditions and management, especially when spatial structure affects resource capture or microclimate.[1]

FSP models may be implemented as static models that represent plant structure at one or more developmental stages, or as dynamic models that simulate structural development and function through time.[1] Some FSP models can also be formulated as individual- or agent-based models, where organs or other plant units are represented as interacting entities and whole-plant behaviour emerges from their local rules.[2]

Over the years, FSP models have been applied across a wide range of domains, including greenhouse horticulture, crop phenotyping, intercropping research, evolutionary biology, forestry, agricultural systems modeling, and virtual plant phenotyping.[3][4][5][1][6]

History and Development

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The development of functional-structural plant models emerged from early mathematical and simulation-based models of plant architecture. Foundational work in the 1960s and 1970s introduced Lindenmayer systems (L-systems), which provided a formal framework for modeling branching structures in plants.[7] From the 1990s onward, structural representations were increasingly coupled with models of physiological processes (e.g., photosynthesis, transport, allocation), forming what are now referred to as FSP models.[8][1][4] As computational power increased and data acquisition technologies like 3D imaging and high-throughput plant phenotyping evolved, FSP models became more refined and versatile.[1][4] Alongside these technological improvements, several software platforms have been developed to support the design and simulation of FSP models.[4]

Core Principles, Modelling Approaches, and Applications

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FSP models typically represent a plant as a set of connected components (e.g., leaves, internodes, roots, reproductive organs) with explicit topology and 3D geometry, coupled to one or more process sub-models describing resource capture and use.[1] This structure allows models to represent feedbacks between architecture (e.g., organ position and orientation) and function (e.g., light absorption and carbon assimilation), and allows for the simulation of interactions between the plant and its environment.[1][4]

FSP models are often grouped into static and dynamic models:

  • Static models describe plant structure at a defined stage (or a small set of stages) and are often used to analyse instantaneous quantities such as light absorption or photosynthetic performance under fixed conditions.[1]
  • Dynamic models simulate development over time and can represent time-dependent feedbacks between architectural development and physiological processes under changing environmental inputs.[1]

Software and Implementation

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Several software platforms support the development and simulation of FSP models, including:

FSP models may also have a stand-alone implementation (i.e., without the use of a platform), such asOpenSimRoot.[16] More stand-alone FSP model implementations, together with non-FSP models, can be found on the website Quantitative Plant[17] Platforms or models may be entirely or partially written in Java, C/C++, Matlab, Scilab, Python, or Julia. Each offers specific capabilities for modeling plant architecture, simulating physiological processes, or visualizing 3D structures.

Community and Resources

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Work on functional–structural plant modelling is supported through research networks, workshops, and special issues that synthesise advances in the field.[6][4] Online repositories are also used to share code, tutorials, and implementation advice. The Functional-Structural Plant Modeling Forum[18] is an online community where researchers, model developers, and students discuss topics related to FSP models, share tools and tutorials, and troubleshoot model implementation issues.

Challenges

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While Functional-Structural Plant Models (FSP models) offer powerful insights into plant growth and development, several challenges remain. Parameterization is often complex, as acquiring accurate physiological and structural data is labor-intensive.[1] Computational demands can also be high, particularly for large or detailed simulations.[4] Ensuring model validation through integration with experimental data is essential but remains a bottleneck.[1][4] Additionally, FSP models often require an interdisciplinary approach, bridging plant science, computer modeling, and mathematics.[6] Advancements in AI, remote sensing, and high-throughput phenotyping are expected to enhance model accuracy and scalability.[19][20]

Examples of FSP Models

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  • FSP Model Dwarf tomatoes YouTube.[21]

See also

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References

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  1. ^ a b c d e f g h i j k l Vos, J.; Evers, J. B.; Buck-Sorlin, G. H.; Andrieu, B.; Chelle, M.; de Visser, P. H. B. (2010). "Functional–structural plant modelling: a new versatile tool in crop science". Journal of Experimental Botany. 61 (8): 2101–2115. doi:10.1093/jxb/erp345. PMID 19995824.
  2. ^ Zhang, Bo; DeAngelis, Donald L. (2020). "An overview of agent-based models in plant biology and ecology". Annals of Botany. 126 (4): 539–557. doi:10.1093/aob/mcaa043. PMC 7489105. PMID 32173742.
  3. ^ Sievänen, Risto; Godin, Christophe; DeJong, Theodore M.; Nikinmaa, Eero (2014). "Functional–structural plant models: a growing paradigm for plant studies". Annals of Botany. 114 (4): 599–603. doi:10.1093/aob/mcu175. PMC 4156128. PMID 25469374.
  4. ^ a b c d e f g h Louarn, Gaëtan; Song, Youhong (2020). "Two decades of functional–structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology". Annals of Botany. 126 (4): 501–509. doi:10.1093/aob/mcaa143. PMC 7489058. PMID 32725187.
  5. ^ de Vries, Jorad (2021). "Using evolutionary functional–structural plant modelling to understand the effect of climate change on plant communities". In Silico Plants. 3 (2) diab029. doi:10.1093/insilicoplants/diab029.
  6. ^ a b c Evers, Jochem B.; Letort, Véronique; Renton, Michael; Kang, Mengzhen (2018). "Computational botany: advancing plant science through functional–structural plant modelling". Annals of Botany. 121 (5): 767–772. doi:10.1093/aob/mcy050. PMC 5906916.
  7. ^ Lindenmayer, A. (1968). "Mathematical models for cellular interactions in development. I. Filaments with one-sided inputs". Journal of Theoretical Biology. 18 (3): 280–299. Bibcode:1968JThBi..18..280L. doi:10.1016/0022-5193(68)90079-9.
  8. ^ Prusinkiewicz, P., & Lindenmayer, A. (1990). The Algorithmic Beauty of Plants. Springer.
  9. ^ De Vos, Dirk; Dzhurakhalov, Abdiravuf; Draelants, Delphine; Bogaerts, Irissa; Kalve, Shweta; Prinsen, Els; Vissenberg, Kris; Vanroose, Wim; Broeckhove, Jan; Beemster, Gerrit T. S. (2012). "Towards mechanistic models of plant organ growth". Journal of Experimental Botany. 63 (9): 3325–3337. doi:10.1093/jxb/ers037. PMID 22371079.
  10. ^ Pradal, C.; Dufour-Kowalski, S.; Boudon, F.; Fournier, C.; Godin, C. (2008). "OpenAlea: a visual programming and component-based software platform for plant modelling". Functional Plant Biology. 35 (10): 751–760. Bibcode:2008FunPB..35..751P. doi:10.1071/FP08084. PMID 32688829.
  11. ^ Kniemeyer, O.; Buck-Sorlin, G. H.; Kurth, W. (2007). "GroIMP as a platform for functional-structural modelling of plants". In Vos, J.; Marcelis, L. F. M.; de Visser, P. H. B.; Struik, P. C.; Evers, J. B. (eds.). Functional-structural plant modelling in crop production. Springer. pp. 43–52. ISBN 978-1-4020-6033-5.
  12. ^ Zhou, Xiao-Ran; Schnepf, Andrea; Vanderborght, Jan; Leitner, Daniel; Lobet, Guillaume; Vereecken, Harry (2020). "CPlantBox, a whole-plant modelling framework for the simulation of water- and carbon-related processes". In Silico Plants. 2 (1): diaa001. doi:10.1093/insilicoplants/diaa001.{{cite journal}}: CS1 maint: article number as page number (link)
  13. ^ Bailey, Brian N. (2019). "Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework". Frontiers in Plant Science. 10 1185. Bibcode:2019FrPS...10.1185B. doi:10.3389/fpls.2019.01185. PMC 6813926. PMID 31681349.
  14. ^ Postma, Jaap A.; Schnepf, Andrea; Keizer, Louise C. P.; Bengtsson, Jenny; Groen, Saskia C.; Smit, Anke L.; Dee, Hans M.; Lynch, Jonathan P. (2017). "OpenSimRoot: widening the scope and application of root architectural models". New Phytologist. 215 (3): 1274–1286. Bibcode:2017NewPh.215.1274P. doi:10.1111/nph.14641. PMC 5575537. PMID 28653341.
  15. ^ Morales, Alejandro; Kottelenberg, David; Ernst, Ana (2025). "Virtual Plant Laboratory: a modern plant modelling framework in Julia". In Silico Plants. 7 (1): diaf005. doi:10.1093/insilicoplants/diaf005.{{cite journal}}: CS1 maint: article number as page number (link)
  16. ^ "OpenSimRoot".
  17. ^ "Quantitative Plant | Plant and Crop models".
  18. ^ "Scientists working with functional-structural plant models". Scientists working with functional-structural plant models.
  19. ^ Smoleňová, Katarína; Vilfan, Nastassia; Bustos-Korts, Daniela; van Daalen, Tim; de Visser, Pieter; Kaiser, Elias; van de Zedde, Rick; Evers, Jochem B. (2025). "Development of a tomato functional-structural plant model for digital twin applications". In Silico Plants. 7 (2): diaf022. doi:10.1093/insilicoplants/diaf022.{{cite journal}}: CS1 maint: article number as page number (link)
  20. ^ Mitsanis, Christos; Hurst, William; Tekinerdogan, Bedir (2024). "A 3D functional plant modelling framework for agricultural digital twins". Computers and Electronics in Agriculture. 218 108733. Bibcode:2024CEAgr.21808733M. doi:10.1016/j.compag.2024.108733.
  21. ^ "Functional-Structural Plant Model of Dwarf Tomatoes". 21 March 2025 – via YouTube.