Skip to main content
SpringerLink
Log in

Role of Virtual Plants in Digital Agriculture

  • Chapter
  • First Online:
Digital Ecosystem for Innovation in Agriculture

Part of the book series: Studies in Big Data ((SBD,volume 121))

  • 317 Accesses

Abstract

Virtual plants (VP) are computer-simulated three-dimensional (3D) models of plants or trees. One approach to create VPs is so-called functional-structural plant models (FSPM), which are used to model an accurate plant shape and architecture and combines it with physiological processes. Static or dynamic FSPMs are a well-established approach to serve as a versatile tool for predicting crop growth patterns in response to variations in environmental conditions. Using VP, crop growth can be simulated (in silico) rapidly compared to its natural growing season, which can take up to a few months or longer. Crop researchers and breeders employ in silico as an alternative to time-consuming, labor-intensive actual field trials to explore the focused growth of crops in specific environments and swiftly quantify essential attributes. This facilitates accelerate selective crop breeding, a promising strategy to address the impending challenge of food security. In VP modeling, first step is to select the study crop and decide on what traits to be examined, then set up a field experiment, gathering temporal data on crop growth, perform statistical analysis of this data to explore the relationship among various parameters, mathematical modeling to establish the growth rules for plant parameters. Finally, the computer simulation of FSPM is used to visualized the study crop at the level of individual stand or canopy. After getting the expected output, researchers can parameterize this model to generate different test scenarios. In-depth analysis of these test cases offers an ideal method to be used in evaluating the performance of the resultant test breed in terms of growth time, yield, resistance to biotic and abiotic stress, etc., as per application. This technique can potentially augment and expedite the existing high-performance plant phenotyping research by generating synthetic datasets to sort out the imbalance in the existing actual plant phenotyping datasets; however, it has a few challenges in its application. This chapter will cover the concept of VP modeling, its applications, and some challenges in its application.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Allen, M., DeJong, T., & Prusinkiewicz, P. (2006). L-PEACH, an L-systems-based model for simulating the architecture and carbon partitioning of growing fruit trees. Acta Horticulturae, 707, 71–76. https://doi.org/10.17660/actahortic.2006.707.8

  • Artzet, S., Chen, T.-W., Chopard, J., Brichet, N., Mielewczik, M., Cohen-Boulakia, S., Cabrera-Bosquet, L., Tardieu, F., Fournier, C. and Pradal, C. (2019). Phenomenal: An automatic open source li-brary for 3D shoot architecture reconstruction and analysis for im-age-based plant phenotyping. https://doi.org/10.1101/805739

  • Barillot, R., Combes, D., Huynh, P. and Escobar-Gutiérrez, A.J. (2010). Analysing light sharing in cereal/legume intercropping sys-tems through Functional Structural Plant Models. In: 6th Interna-tional Workshop on Functional-Structural Plant Models,University of California, Davis.

    Google Scholar 

  • Buck-Sorlin, G. (2013a). Functional-structural plant modeling. Encyclopedia of Systems Biology, 778–781. https://doi.org/10.1007/978-1-4419-9863-7_1479

  • Buck-Sorlin, G. (2013b). Process-based model. In Encyclopedia of systems biology (pp. 1755–1755). https://doi.org/10.1007/978-1-4419-9863-7_1545

  • Chandramouli, M., Narayanan, B. & Bertoline, G. R. (2013). A graphics design framework to visualize multi-dimensional eco-nomic datasets. The Engineering Design Graphics Journal, 77(3).

    Google Scholar 

  • Chelle, M., & Andrieu, B. (1998). The nested radiosity model for the distribution of light within plant canopies. Ecological Modelling, 111(1), 75–91. https://doi.org/10.1016/s0304-3800(98)00100-8

    Article  Google Scholar 

  • Chelle, M., Evers, J. B., Combes, D., Varlet-Grancher, C., Vos, J., & Andrieu, B. (2007). Simulation of the three-dimensional distribution of the red:far-red ratio within crop canopies. New Phytologist, 176(1), 223–234. https://doi.org/10.1111/j.1469-8137.2007.02161.x

    Article  Google Scholar 

  • Clausnitzer, V., & Hopmans, J. W. (1994). Simultaneous modeling of transient three-dimensional root growth and soil water flow. Plant and Soil, 164(2), 299–314. https://doi.org/10.1007/bf00010082

    Article  Google Scholar 

  • Dauzat, J., & Eroy, M. N. (1997). I am simulating light regime and intercrop yields in coconut-based farming systems. European Journal of Agronomy, 7(1–3), 63–74. https://doi.org/10.1016/s1161-0301(97)00029-4

    Article  Google Scholar 

  • Danzi, D., Briglia, N., Petrozza, A., Summerer, S., Povero, G., Stivaletta, A., Cellini, F., Pignone, D., De Paola, D. and Janni, M. (2019). Can High Throughput Phenotyping Help Food Security in the Mediterranean Area? Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.00015

  • de Reffye, P., Barthélémy, D., Blaise, F., Fourcaud, T. and Houllier, F. (1997). A functional model of tree growth and tree architecture. Silva Fennica, 31(3). https://doi.org/10.14214/sf.a8529

  • de Reffye, P., Blaise, F., Chemouny, S., Jaffuel, S., Fourcaud, T., & Houllier, F. (1999). Calibration of a hydraulic architecture-based growth model of cotton plants. Agronomie, 19(3–4), 265–280. https://doi.org/10.1051/agro:19990307

    Article  Google Scholar 

  • de Reffye, P., Blaise, F., & Houllier, F. (1998). Modeling plant growth and architecture: recent advances and applications to agronomy and forestry. Acta Horticulturae, 456, 105–116. https://doi.org/10.17660/actahortic.1998.456.12

  • de Wit, C. T. (1982). Simulation of living systems. In Simulation of plant growth and crop production Pudoc (pp. 3–8).

    Google Scholar 

  • Donald, C. M. (1968). The breeding of crop ideotypes. Euphytica, 17(3), 385–403. https://doi.org/10.1007/bf00056241

    Article  Google Scholar 

  • Ehrlich, P. R., Ehrlich, A. H., & Daily, G. C. (1993). Food security, population and environment. Population and Development Review, 19(1). https://doi.org/10.2307/2938383

  • Evers, J.B., Vos, J., Fournier, C., Andrieu, B., Chelle, M. and Stru-ik, P.C. (2005). Towards a generic architectural model of tillering in Gramineae, as exemplified by spring wheat ( Triticum aestivum ). New Phytologist, 166(3), pp.801–812. https://doi.org/10.1111/j.1469-8137.2005.01337.x

  • Evers, J. B., Vos, J., Andrieu, B., & Struik, P. C. (2006). Cessation of tillering in spring wheat about light interception and red: far-red ratio. Annals of Botany, 97(4), 649–658. https://doi.org/10.1093/aob/mcl020

    Article  Google Scholar 

  • Federl, P., & Prusinkiewicz, P. (1999). Virtual laboratory: an interactive software environment for computer graphics. In Proceedings—Computer Graphics International, CGI.

    Google Scholar 

  • Fournier, C., & Andrieu, B. (1999). ADEL-maize: an L-system-based model for integrating growth processes from the organ to the canopy. Application to the regulation of morphogenesis by light availability. Agronomie, 19(3–4), 313–327. https://doi.org/10.1051/agro:19990311

  • Godin, C., Costes, E., & Sinoquet, H. (1999). A method for describing plant architecture which integrates topology and geometry. Annals of Botany, 84(3), 343–357. https://doi.org/10.1006/anbo.1999.0923

    Article  Google Scholar 

  • Godin, C., & Sinoquet, H. (2005). Functional-structural plant modelling. New Phytologist, 166(3), 705–708. https://doi.org/10.1111/j.1469-8137.2005.01445.x

    Article  Google Scholar 

  • Guo, Y., & Li, B. (2001). New advances in virtual plant research. Chinese Science Bulletin, 46(11), 888–894. https://doi.org/10.1007/bf02900459

    Article  Google Scholar 

  • Hanan, J.S. and Room, P.M. (1997). Practical aspects of virtual plant research. In: In: Plants to Ecosystems - Advances in Computa-tional Life Sciences. [online] CSIRO Publishing, p.Chapter 2:28-44; 25 refs; illus. Available at: http://hdl.handle.net/102.100.100/220457?index=1

  • Henke, M., & Buck-Sorlin, G. H. (2017). Using a full spectral raytracer for calculating light microclimate in functional-structural plant modelling. Computing and Informatics, 36(6), 1492–1522. Available via DIALOG. https://www.cai.sk/ojs/index.php/cai/article/view/2017_6_1492

  • Heuvelink, E. (1996). Dry matter partitioning in tomato: Validation of a dynamic simulation model. Annals of Botany, 77(1), 71–80. https://doi.org/10.1006/anbo.1996.0009

    Article  Google Scholar 

  • Heuvelink, E. (1999). Evaluation of a dynamic simulation model for tomato crop growth and development. Annals of Botany, 83(4), 413–422. https://doi.org/10.1006/anbo.1998.0832

    Article  Google Scholar 

  • Hitz, T., Henke, M., Graeff-Hönninger, S., & Munz, S. (2019). Three-dimensional simulation of light spectrum and intensity within an LED growth chamber. Computers and Electronics in Agriculture, 156, 540–548. https://doi.org/10.1016/j.compag.2018.11.043

    Article  Google Scholar 

  • Huwe, T. and Hemmerling, R. (2008). Stochastic path tracing on consumer graphics cards. Proceedings of the 24th Spring Confer-ence on Computer Graphics. https://doi.org/10.1145/1921264.1921287

  • Huxley, J. S. (1932). Problems of relative growth (p. 273). Johns Hopkins University Press.

    Google Scholar 

  • Jallas, E., Martin, P., Sequeira, R., Turner, S., Cretenet, M., & Gérardeaux, E. (2000). Virtual COTONS®, the firstborn of the next generation of simulation model. In Virtual worlds (pp. 235–244). https://doi.org/10.1007/3-540-45016-5_22

  • Kang, M., Evers, J. B., Vos, J., & de Reffye, P. (2007). The derivation of sink functions of wheat organs using the GreenLab model. Annals of Botany, 101(8), 1099–1108. https://doi.org/10.1093/aob/mcm212

    Article  Google Scholar 

  • Karwowski, R. and Lane, B. (2004). L-studio 4.0 user’s guide. [online] Available at: http://www.cpsc.ucalgary.ca/Research/bmv/lstudio

  • Karwowski, R., & Prusinkiewicz, P. (2003). Design and implementation of the L+C modeling language. Electronic Notes in Theoretical Computer Science, 86(2), 134–152. https://doi.org/10.1016/s1571-0661(04)80680-7

    Article  Google Scholar 

  • Kirby, E.J.M. (1988). Analysis of leaf, stem and ear growth in wheat from terminal spikelet stage to anthesis. Field Crops Re-search, 18(2–3), pp.127–140. https://doi.org/10.1016/0378-4290(88)90004-4

  • Kniemeyer, O., Buck-Sorlin, G. and Kurth, W. (2007). The GroIMP is a platform for the functional-structural modelling of plants. In Functional-structural plant modelling in crop production (pp. 43–52). https://doi.org/10.1007/1-4020-6034-3_4

  • Kurth, W., & Sloboda, B. (1997). Growth grammars simulate trees—An extension of L-systems incorporating local variables and sensitivity. Silva Fennica, 31(3). https://doi.org/10.14214/sf.a8527

  • Lindenmayer, A. (1968a). Mathematical models for cellular interactions in development I. Filaments with one-sided inputs. Journal of Theoretical Biology, 18(3), 280–299. https://doi.org/10.1016/0022-5193(68)90079-9

  • Lindenmayer, A. (1968b). Mathematical models for cellular interactions in development II. Simple and branching filaments with two-sided inputs. Journal of Theoretical Biology, 18(3), 300–315. https://doi.org/10.1016/0022-5193(68)90080-5

  • Lopez, G., Favreau, R.R., Smith, C. and DeJong, T.M. (2010). L-PEACH: A Computer-based Model to Understand How Peach Trees Grow. HortTechnology, 20(6), pp.983–990. https://doi.org/10.21273/hortsci.20.6.983

  • Lv, M. M., Lu, S. L., Guo, X. Y. (2015). Interactive virtual fruit tree pruning simulation. In: Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering (pp. 78–681). Atlantis Press.

    Google Scholar 

  • Lynch, J. P., Nielsen, K. L., Davis, R. D., & Jablokow, A. G. (1997). SimRoot: Modelling and visualization of root systems. Plant and Soil, 188(1), 139–151.

    Article  Google Scholar 

  • Marshall-Colon, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Benes, B., von Caemmerer, S., Christensen, A. J., Cox, D. J., Hart, J. C., Hirst, P. M., Kannan, K., Katz, D. S., Lynch, J. P., Millar, A. J., Panneerselvam, B., Price, N. D., Prusinkiewicz, P., Raila, D., & Shekar, R. G. (2017). Crops in silico: Generating virtual crops using an integrative and multi-scale modeling platform. Frontiers in Plant Science, 8, 786. https://doi.org/10.3389/fpls.2017.00786

    Article  Google Scholar 

  • Martre, P., Quilot-Turion, B., Luquet, D., Memmah, M.-M.O.-S., Chenu, K., & Debaeke, P. (2015). Model-assisted phenotyping and ideotype design. In Crop physiology (pp. 349–373). https://doi.org/10.1016/b978-0-12-417104-6.00014-5

  • McKinnon, J. M., Baker, D. N., Whisler, F. D., & Lambert, J. R. (1989). Application of the GOSSYM/COMAX system to cotton crop management. Agricultural Systems, 31(1), 55–65. https://doi.org/10.1016/0308-521x(89)90012-7

    Article  Google Scholar 

  • Mech, R., Prusinkiewicz, P. (1996). Visual models of plants interacting with their environment. In Computer Graphics Proceedings, Annual Conference Series. New York: ACM SIGGRAPH.

    Google Scholar 

  • Miao, C., Guo, A., Thompson, A. M., Yang, J., Ge, Y., Schnable, J. C. (2021). Automation of leaf counting in maize and sorghum using deep learning. The Plant Phenome Journal, 4(1). https://doi.org/10.1002/ppj2.20022

  • Mündermann, L., Erasmus, Y., Lane, B., Coen, E., & Prusinkiewicz, P. (2005). Quantitative modeling of arabidopsis development. Plant Physiology, 139(2), 960–968. https://doi.org/10.1104/pp.105.060483

    Article  Google Scholar 

  • Perttunen, J., Sievänen, R., & Nikinmaa, E. (1998). LIGNUM: A model is combining the structure and the functioning of trees. Ecological Modelling, 108(1–3), 189–198. https://doi.org/10.1016/s0304-3800(98)00028-3

    Article  Google Scholar 

  • Pradal, C., Dufour-Kowalski, S., Boudon, F., Fournier, C., & Godin, C. (2008). OpenAlea: Visual programming and component-based software platform for plant modeling. Functional Plant Biology, 35(10), 751. https://doi.org/10.1071/fp08084

    Article  Google Scholar 

  • Prusinkiewicz, P. (2004). Art and science of life: designing and growing virtual plants with L-systems. Acta Horticulturae, 630, 15–28. https://doi.org/10.17660/actahortic.2004.630.1

  • Radoslaw, K., & Przemyslaw, P. (2004). The L-System-based plant-modeling environment L-Studio 4.0. In Proceedings of the 4th International Workshop on Functional and Structural Plant Models, Montpellier, France (pp. 403–405).

    Google Scholar 

  • Richards, O. W., & Kavanagh, A. J. (1943). The analysis of the relative growth gradients and changing form of growing organisms: Illustrated by the tobacco leaf. The American Naturalist, 77(772), 385–399. https://doi.org/10.1086/281140

    Article  Google Scholar 

  • Room, P., Hanan, J., & Prusinkiewicz, P. (1996). Virtual plants: New perspectives for ecologists, pathologists, and agricultural scientists. Trends in Plant Science, 1(1), 33–38. https://doi.org/10.1016/s1360-1385(96)80021-5

    Article  Google Scholar 

  • Seleznyova, A. N., Saei, A., Han, L., & van Hooijdonk, B. M. (2018). From field data to modeling concepts: building a mechanistic FSPM for apple. In 2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA). https://doi.org/10.1109/pma.2018.8611582

  • Simon, L., & Steppe, K. (2019). Application of a functional-structural plant model on two different wheat varieties to enhance physiological interpretation. In Master of Science in de bio-ingenieurswetenschappen:landbouwkunde. https://lib.ugent.be/catalog/rug01:002791221

  • Smith, G. S., Curtis, J. P., & Edwards, C. M. (1992). A method for analyzing plant architecture as it relates to fruit quality using three-dimensional computer graphics. Annals of Botany, 70(3), 265–269. https://doi.org/10.1093/oxfordjournals.aob.a088468

    Article  Google Scholar 

  • Swinehart, D.F. (1962). The Beer-Lambert Law. Journal of Chemical Education, 39(7), p.333. https://doi.org/10.1021/ed039p333

  • Vos, J., Evers, J. B., Buck-Sorlin, G. H., Andrieu, B., Chelle, M., & de Visser, P. H. B. (2009). Functional–structural plant modeling: A new versatile tool in crop science. Journal of Experimental Botany, 61(8), 2101–2115. https://doi.org/10.1093/jxb/erp345

    Article  Google Scholar 

  • Yan, H.-P. (2004). A dynamic, architectural plant model simulating resource-dependent growth. Annals of Botany, 93(5), 591–602. https://doi.org/10.1093/aob/mch078

    Article  Google Scholar 

  • Zhang, Y., Henke, M., Buck-Sorlin, G. H., Li, Y., Xu, H., Liu, X., & Li, T. (2021). I am estimating canopy leaf physiology of tomato plants grown in a solar greenhouse: Evidence from simulations of light and thermal microclimate using a Functional-Structural Plant Model. Agricultural and Forest Meteorology, 307, 108494. https://doi.org/10.1016/j.agrformet.2021.108494

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre of Studies in Resources Engineering (CSRE), Indian Institute of Technology Bombay, Mumbai, 400076, India

    Suchitra M. Patil & Adinarayana Jagarlapudi

  2. School of Agronomy, Hunan Agricultural University, Changsha, 410128, People’s Republic of China

    Michael Henke

  3. Computer Graphics Technology, Purdue University NW, Hammond, IN, 46323, USA

    Magesh Chandramouli

Authors
  1. Suchitra M. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Henke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Magesh Chandramouli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adinarayana Jagarlapudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suchitra M. Patil .

Editor information

Editors and Affiliations

  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Sanjay Chaudhary

  2. CIFOR-ICRAF India, Asia Continental Program, New Delhi, Delhi, India

    Chandrashekhar M. Biradar

  3. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Srikrishnan Divakaran

  4. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Mehul S. Raval

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Patil, S.M., Henke, M., Chandramouli, M., Jagarlapudi, A. (2023). Role of Virtual Plants in Digital Agriculture. In: Chaudhary, S., Biradar, C.M., Divakaran, S., Raval, M.S. (eds) Digital Ecosystem for Innovation in Agriculture. Studies in Big Data, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-99-0577-5_8

Download citation

Publish with us

Policies and ethics

Search

Navigation