Identification of virtual plants using bayesian networks based on parametric L-system

(1) * Suhartono Suhartono Mail (Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia)
(2) Fachrul Kurniawan Mail (Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia)
(3) Bahtiar Imran Mail (Sekolah Tinggi Manajemen Informatika Komputer, Mataram, Indonesia)
*corresponding author


Parametric L-System is a method for modelling virtual plants. Virtual plant modelling consists of components of axiom and production rules for alphabets in parametric L-System. Generally, to get the alphabet in parametric L-System, one would guess the production rules and perform a modification on the axiom. The objective of this study was to build virtual plant that was affected by the environment. The use of Bayesian networks was to extract the information structure of the growth of a plant as affected by the environment. The next step was to use the information to generate axiom and production rules for the alphabets in the parametric L-System. The results of program testing showed that among the five treatments, the combination of organic and inorganic fertilizer was the environmental factor for the experiment. The highest result of 6.41 during evaluation of the virtual plant came from the treatment with combination of high level of organic fertilizer and medium level of inorganic fertilizer. Mean error between real plant and virtual plan was 9.45 %.


plant growth modeling, Bayesian network, environment, distribution probability and L-system



Article metrics

Abstract views : 1936 | PDF views : 334




Full Text



[1] A. Lindenmayer, “Mathematical models for cellular interactions in development I. Filaments with one-sided inputs,” J. Theor. Biol., vol. 18, no. 3, pp. 280–299, 1968, doi:

[2] P. Prusinkiewicz and A. Lindenmayer, The algorithmic beauty of plants, 1990, 1st ed., doi:

[3] P. Prusinkiewicz, P. Federl, R. Karwowski, and R. Mech, “L-systems and beyond,” Course notes from SIGGRAPH, vol. 5, pp. 70–82, 2003, available at :

[4] V. Clausnitzer and J. W. Hopmans, “Simultaneous modeling of transient three-dimensional root growth and soil water flow,” Plant Soil, vol. 164, no. 2, pp. 299–314, 1994, doi:

[5] Y. Sun, Y. Liang, and Q. Wu, “Visualization of plant growth based on l-system,” in World Automation Congress (WAC), 2010, 2010, pp. 299–304, available at :

[6] R. Měch and P. Prusinkiewicz, “Visual models of plants interacting with their environment,” in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, 1996, pp. 397–410, doi:

[7] C. Chen, G. Ji, and B. Zhao, “Learning text to model: A Bayesian network based L-system modeling strategy,” in Behavioral, Economic and Socio-cultural Computing (BESC), 2016 International Conference on, 2016, pp. 1–2, doi:

[8] Y. Guo, G. Bai, and Y. Hu, “Using bayes network for prediction of type-2 diabetes,” in Internet Technology And Secured Transactions, 2012 International Conference for, 2012, pp. 471–472, available at :

[9] X. Yu, J. Liu, Z. Yang, and X. Liu, “The Bayesian Network based program dependence graph and its application to fault localization,” J. Syst. Softw., vol. 134, pp. 44–53, 2017, doi:

[10] H. Liu, S. Zhou, W. Lam, and J. Guan, “A new hybrid method for learning bayesian networks: Separation and reunion,” Knowledge-Based Syst., vol. 121, pp. 185–197, 2017, doi:

[11] E. Castellanos, F. Ramos, and M. Ramos, “Semantic death in plant’s simulation using Lindenmayer systems,” in Natural Computation (ICNC), 2014 10th International Conference on, 2014, pp. 360–365, doi:

[12] Y. Rodkaew, S. Chuai-Aree, S. Siripant, C. Lursinsap, and P. Chongstitvatana, “Animating plant growth in L-system by parametric functional symbols,” Int. J. Intell. Syst., vol. 19, no. 1–2, pp. 9–23, 2004, doi:

[13] R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and statistics for engineers and scientists. Prentice Hall, 2012.

[14] N. Everitt, G. Bottegal, C. R. Rojas, and H. Hjalmarsson, “Identification of modules in dynamic networks: An empirical Bayes approach,” in Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 4612–4617, doi:

[15] E. Zarei, A. Azadeh, N. Khakzad, M. M. Aliabadi, and I. Mohammadfam, “Dynamic safety assessment of natural gas stations using Bayesian network,” J. Hazard. Mater., vol. 321, pp. 830–840, 2017, doi:

[16] R. J. McNally, P. Mair, B. L. Mugno, and B. C. Riemann, “Co-morbid obsessive--compulsive disorder and depression: A Bayesian network approach,” Psychol. Med., vol. 47, no. 7, pp. 1204–1214, 2017, doi:

[17] N. Noyes, K.-C. Cho, J. Ravel, L. J. Forney, and Z. Abdo, “Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis,” PLoS One, vol. 13, no. 1, p. e0191625, 2018, doi:

[18] A. Darwiche, Modeling and reasoning with Bayesian networks. Cambridge University Press, 2009, available at:

[19] E. Azimirad and J. Haddadnia, “Target threat assessment using fuzzy sets theory,” Int. J. Adv. Intell. Informatics, vol. 1, no. 2, pp. 57–74, 2015, doi:

[20] C. Jacob, Illustrating evolutionary computation with Mathematica. Morgan Kaufmann, 2001, available at:

[21] C. Chen, J. Twycross, and J. M. Garibaldi, “A new accuracy measure based on bounded relative error for time series forecasting,” PLoS One, vol. 12, no. 3, p. e0174202, 2017, doi:

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
E: (paper handling issues) (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0