Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

  • Sotirios A Tsaftaris
  • Christos Noutsos
Chapter
Part of the Environmental Science and Engineering book series (ESE)

Abstract

In this paper we discuss a prototype, easy-to-deploy, and low cost (∼ $250) phenotype collection system for growth chambers. Off the shelf digital cameras, wireless transmitters, and PCs are used to store and process the images. A Matlab pipeline is used to fuse multiple images, identify the location of each Arabidopsis plant, segment its leaves, and measure leaf topology and area. Our early findings (unpublished) using this system for correlating genotype to phenotype are very promising. We hope that with future improvements and broad adoption, it will have the same disruptive effects as the first “build your own” microarrayers, which allowed for the explosion of genotyping information. Low cost genotyping and phenotyping will hopefully address some of the environmental, agricultural, and industrial sustainability challenges we are facing today.

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Notes

Acknowledgements

We would like to thank Prof. Borevitz (Department of Ecology and Evolution at University of Chicago) for providing us with the images and initial funding for this effort. Finally, we should thank Ron Hause, a graduate student from the Committee on Genetics, Genomics, and Systems Biology who did his rotation in Prof. Borevitz’s Lab, for his assistance in the development of this project.

References

  1. Nathan P. Gillett, Dáithí A. Stone, Peter A. Stott, Toru Nozawa, Alexey Yu. Karpechko1, Gabriele C. Hegerl, Michael F. Wehner & Philip D. Jones (2008). Attribution of polar warming to human influence. Nature Geoscience. 1. 750-754CrossRefGoogle Scholar
  2. Pierre Crosson (1997). Impacts of Climate Change on Agriculture. Climate Issues Brief 4.Google Scholar
  3. Pittock, B. (2003).Climate change: An Australian Guide to the Science and Potential Impacts, Australian Greenhouse Office.Google Scholar
  4. Granier, et al., (2005). PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist. 169(3), 623-635.CrossRefGoogle Scholar
  5. Hilbert, D.W., Ostendorf, B. and Hopkins, M., (2001).Sensitivity of tropical forests to climate change in the humid tropics of North Queensland. Austral Ecology. 26, 590–603.CrossRefGoogle Scholar
  6. William E. McClain (1997). Prairie establishment and landscaping. Nature Heritage Technical Publication.Google Scholar
  7. Burel, F. and Baudry, J., (1995). Species biodiversity in changing agricultural landscapes: A case study in the Pays d’Auge France. Agric. Ecosyst. Environ. 55, pp. 193–200.CrossRefGoogle Scholar
  8. Edwards, D. and Batley, J. (2004). Plant bioinformatics: from genome to phenome. Trends in Biotechnology, 22(5):232-237.CrossRefGoogle Scholar
  9. Lussier, Y. A. and Liu, Y. (2007). Computational approaches to phenotyping: high-throughput phenomics. Proc Am Thorac Soc, 4(1):18-25.CrossRefGoogle Scholar
  10. Aurelie Bonin (2008). Population Genomics:a new generation of genome scans to bridge the gap with functional genomics. Molecular ecology. 17, 3583 – 3584.CrossRefGoogle Scholar
  11. Nosil P, Egan, SR, Funk DJ (2008). Heterogeneous genomic differentiation between walking-stick ecotypes: ’isolation by adaptation’ and multiple roles for divergent selection. Evolution.62,316-336.CrossRefGoogle Scholar
  12. Boyes, D. C., Zayed, A. M., Ascenzi, R., Mccaskill, A. J., Hoffman, N. E., Davis, K. R., and Gorlach, J. (2001). Growth stage-based phenotypic analysis of arabidopsis: A model for high throughput functional genomics in plants. Plant Cell.13(7):1499-1510.CrossRefGoogle Scholar
  13. Tim Anderson (2009). How to Make Your Own Waterproof Camera Enclosure. Online [http://web.media.mit.edu/~tim/pix/waterproofcamera.html]
  14. Ronald J. Hause (2008). Deleting images with interval shooting. Online. [http://chdk.setepontos.com/index.php/topic,2003.0.html]
  15. Maik, V., Cho, D., Shin, J., and Paik, J. (2007). Regularized restoration using image fusion for digital auto-focusing. Circuits and Systems for Video Technology, IEEE Transactions on. 17(10):1360-1369.CrossRefGoogle Scholar
  16. Burt, P. J. and Kolczynski, R. J. (1993). Enhanced image capture through fusion. Computer Vision, 1993. Proceedings., Fourth International Conference on. pp 173-182.Google Scholar
  17. De, I. and Chanda, B. (2006). A simple and efficient algorithm for multifocus image fusion using morphological wavelets. Signal Process. 86(5):924-936.CrossRefGoogle Scholar
  18. Blum, R. (2005). Robust image fusion using a statistical signal processing approach. Information Fusion. 6(2):119-128.CrossRefGoogle Scholar
  19. Kim, S. K. and Paik, J. K. (1998). Out-of-focus blur estimation and restoration for digital auto-focusing system. Electronics Letters. 34(12):1217-1219.CrossRefGoogle Scholar
  20. Wu, X. (1992). Color quantization by dynamic programming and principal analysis. ACM Trans. Graph. 11(4):348-372.CrossRefGoogle Scholar
  21. Wikipedia contributors (2008) YIQ. Wikipedia, The Free Encyclopedia. Online http://en.wikipedia.org/w/index.php?title=YIQ&oldid=259429685
  22. Li, C., Xu, C., Gui, C., and Fox, M. D. (2005). Level set evolution without re-initialization: a new variational formulation. CVPR 2005. IEEE Computer Society Conference on. 1: 430 – 436.Google Scholar
  23. Fitzgibbon, A., Pilu, M., and Fisher, R. B. (1999). Direct least square fitting of ellipses. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 21(5):476-480.CrossRefGoogle Scholar
  24. Mai, F., Hung, Y., Zhong, H., and Sze, W. (2008). A hierarchical approach for fast and robust ellipse extraction. Pattern Recognition. 41(8):2512-2524.CrossRefGoogle Scholar
  25. Mokhtarian, F. and Abbasi, S. (2004). Matching shapes with self-intersections:application to leaf classification. Image Processing, IEEE Transactions on. 13(5):653-661.CrossRefGoogle Scholar
  26. Parvin, B., Yang, Q., Fontenay, G., and Barcellos-Hoff, M. H. (2002). Biosig: an imaging bioinformatic system for studying phenomics. Computer. 35(7):65-71.CrossRefGoogle Scholar
  27. DeRisi, J., Iyer, V, and Brown, P.O. (1998), The MGuide: A Complete Guide to Building Your Own Microarrayer. Stanford, CA, Stanford University 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sotirios A Tsaftaris
    • 1
  • Christos Noutsos
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceNorthwestern University EvanstonUS
  2. 2.Ecology and Evolution DepartmentUniversity of ChicagoChicagoUS

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