Summary
Growth chambers are used by agronomists for various experiments on plants. Because light impinging on plants is one of the main parameters of their growth, inhomogeneity in light reception can provide large bias in the experiments. In this paper we present the first steps of a framework which aims at computing the best locations of light sources that could ensure an homogeneous lighting at some places in these chambers. For this purpose we extent the capabilities of a global illumination approach dedicated to growth chambers. We use Genetic Algorithms with simple source and material models in order to find good light sources locations. Our first results show that it is possible to find such interesting location and that they improve the lighting distribution on the experiment tables used in these chambers.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Boonen, C., Samson, R., Janssens, K., Pien, H., Lemeur, R., Berckmans, D.: Scaling the spatial distribution of photosynthesis from leaf to canopy in a plant growth chamber. Ecological Modelling 156, 201–212 (2002)
Cavazzoni, J., Volk, T., Tubiello, F., Monje, O.: Modelling the effect of diffuse light on canopy photosynthesis in controlled environments. In: Acta Horticulturae (2002)
Chelle, M., Demirel, M., Renaud, C.: Towards a 3d light model for growth chambers using an experiment-assisted design. In: 4th International Workshop on Functional-Structural Plant Models, Montpellier (2004)
Chelle, M., Renaud, C., Delepoulle, S., Combes, D.: Modeling light phylloclimate within growth chambers. In: 5th International Workshop on Functional-Structural Plant Models, Napier, NZ (November 2007)
Costa, L., Oliveira, P.: An evolution strategy for multiobjective optimization (2002)
De Jong, K.: An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan (1975)
Ferentinos, K.P., Albright, L.D.: Optimal design of plant lighting system by genetic algorithms. Engineering Applications of Artificial Intelligence (2005)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. Wiley-Interscience, Chichester (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Goldberg, D.E.: From genetic and evolutionary optimization to the design of conceptual machines. Evolutionary Optimization 1(1), 1–12 (1999)
Holland, J.H.: Report of the systems analysis research group sys. (1975)
Jensen, H.W.: Global Illumination Using Photon Maps. In: Proceedings of the Seventh Eurographics Workshop on Rendering Techniques 1996, pp. 21–30. Springer, New York (1996)
Jensen, H.W.: Realistic Image Synthesis Using Photon Mapping. A. K. Peters LTD, Natick, Massachussets (2001) ISBN 1-56881-147-0
Jolivet, V., Plemenos, D., Poulingeas, P.: Inverse direct lighting with a monte carlo method and declarative modelling. In: ICCS 2002: Proceedings of the International Conference on Computational Science-Part II, London, UK, pp. 3–12. Springer, Heidelberg (2002)
James, T.: Kajiya. The rendering equation. In: Computer Graphics (Proceedings of SIGGRAPH 1986), pp. 143–150 (August 1986)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Lafortune, E.P., Willems, Y.D.: Bi-directional Path Tracing. In: Santo, H.P. (ed.) Proceedings of Third International Conference on Computational Graphics and Visualization Techniques (Computergraphics 1993), Alvor, Portugal, pp. 145–153 (1993)
Languénou, E., Bouatouch, K., Chelle, M.: Global illumination in presence of participating media with general properties. In: Proceedings of the Fifth Eurographics Workshop on Rendering Techniques 1994, pp. 69–85. Springer, New York (1994)
Lee, Z.-J., Su, S.-F., Chuang, C.-C., Liu, K.-H.: Genetic algorithm with ant colony optimization (ga-aco) for multiple sequence alignment. Appl. Soft Comput. 8(1), 55–78 (2008)
Measures, M., Weinberger, P., Baer, H.: Variability of plant growth within controlled-environment chambers as related to temperature and light distribution. Canadian Journal of Plant Science 53 (1973)
Vafaie, H., Imam, I.: feature selection methods: Genetic algorithms vs greedy-like search. In: Proceedings of the International Conference on Fuzzy and Intelligent Control Systems (1994)
Veach, E., Leonidas, J.: Guibas. bidirectionnal estimators for light transport. In: 5th Eurographics Workshop on Rendering, pp. 147–162, juin (1994)
Veach, E., Guibas, L.J.: Metropolis light transport. In: 31st Annual Conference Series on Computer Graphics, pp. 65–76 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Delepoulle, S., Renaud, C., Chelle, M. (2009). Improving Light Position in a Growth Chamber through the Use of a Genetic Algorithm. In: Plemenos, D., Miaoulis, G. (eds) Artificial Intelligence Techniques for Computer Graphics. Studies in Computational Intelligence, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85128-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-85128-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85127-1
Online ISBN: 978-3-540-85128-8
eBook Packages: EngineeringEngineering (R0)