Machine Vision and Applications

, Volume 24, Issue 3, pp 619–636 | Cite as

A high throughput robot system for machine vision based plant phenotype studies

  • Ram Subramanian
  • Edgar P. Spalding
  • Nicola J. Ferrier
Open Access
Original Paper


This work demonstrates how a high throughput robotic machine vision systems can quantify seedling development with high spatial and temporal resolution.The throughput that the system provides is high enough to match the needs of functional genomics research. Analyzing images of plant seedlings growing and responding to stimuli is a proven approach to finding the effects of an affected gene. However, with 104 genes in a typical plant genome, comprehensive studies will require high throughput methodologies. To increase throughput without sacrificing spatial or temporal resolution, a 3 axis robotic gantry system utilizing visual servoing was developed. The gantry consists of direct drive linear servo motors that can move the cameras at a speed of 1 m/s with an accuracy of 1 μm, and a repeatability of 0.1 μm. Perpendicular to the optical axis of the cameras was a 1 m2 sample fixture holds 36 Petri plates in which 144 Arabidopsis thaliana seedlings (4 per Petri plate) grew vertically along the surface of an agar gel. A probabilistic image analysis algorithm was used to locate the root of seedlings and a normalized gray scale variance measure was used to achieve focus by servoing along the optical axis. Rotation of the sample holder induced a gravitropic bending response in the roots, which are approximately 45 μm wide and several millimeter in length. The custom hardware and software described here accurately quantified the gravitropic responses of the seedlings in parallel at approximately 3 min intervals over an 8-h period. Here we present an overview of our system and describe some of the necessary capabilities and challenges to automating plant phenotype studies.


High throughput Visual servoing Focusing Plant phenotyping Image segmentation 


Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.


  1. 1.
    Basu P., Pal J., Lynch J., Brown K.: A novel image-analysis technique for kinematic study of growth and curvature. Plant Physiol. 145, 305–316 (2007)CrossRefGoogle Scholar
  2. 2.
    Canny J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  3. 3.
    Chaumette F., Hutchinson S.: Visual servo control. Part I: basic approaches. IEEE Robot. Autom. Mag. 13(4), 82–90 (2006)CrossRefGoogle Scholar
  4. 4.
    Chaumette F., Hutchinson S.: Visual servo control. Part II: advanced approaches. IEEE Robot. Autom. Mag. 14(1), 109–118 (2007)CrossRefGoogle Scholar
  5. 5.
    Corke, P., Hutchinson, S.: A new hybrid image-based visual servo control scheme. In: Proceedings of the 39th IEEE Conference on Decision and control (2000)Google Scholar
  6. 6.
    Corke P., Hutchinson S.: A new partitioned approach to image-based visual servo control. IEEE Trans. Robot. Autom. 17(4), 507–515 (2001)CrossRefGoogle Scholar
  7. 7.
    French A., Ubeda-Tomas S., Holman T., Bennett M., Pridmore T.: High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiol. 150, 1784–1795 (2009)CrossRefGoogle Scholar
  8. 8.
    Groen, F.C., Young, I.T., Ligthart, G.: A comparison of different focus functions for use in autofocus algorithms. Cytometry, pp. 623–691 (1985)Google Scholar
  9. 9.
    Hutchinson, S.A., Hager, G.D., Corke, P.I.: A tutorial on visual servo control. IEEE Trans. Robot. Autom. 12(5), 651–670 (1996). Google Scholar
  10. 10.
    Ishikawa H., Evans M.: Novel software for analysis of gravitropism: comparative response patters of Arabidopsis wide-type and axr1 seedlings. Plant Cell Environ. 20, 919–928 (1997)CrossRefGoogle Scholar
  11. 11.
    Jaffe M., Wakwfield A., Telewski F., Gulley E., Biro R.: Computer-assisted image analysis of plant growth, thigmomorphogenesis and gravitropism. Plant Physiol. 77, 722–730 (1985)CrossRefGoogle Scholar
  12. 12.
    Kristan M., Pers J., Perse M., Kovacic S.: A bayes spectral entropy based measure of camera focus using a discrete cosine transform. Pattern Recognit. Lett. 27, 1431–1439 (2006)CrossRefGoogle Scholar
  13. 13.
    Krotkov E.: Focusing. IJCV 1, 223–237 (1987)CrossRefGoogle Scholar
  14. 14.
    LemnaTec: (1998)
  15. 15.
    Miller N., Parks B., Spalding E.: Computer-vision analysis of seedling responses to light and gravity. Plant J. 52(2), 374–381 (2007)CrossRefGoogle Scholar
  16. 16.
    Mullen J., Wolverton C., Ishikawa H., Evans M.: Kinetics of constant gravitropic stimulus responses in Arabidopsis roots using a feedback system. Plant Physiol. 123, 665–670 (2000)CrossRefGoogle Scholar
  17. 17.
    Nathaniel, N.K.C., Neow, P.A., M.H.A. Jr.: Practical issues in pixel-based autofocusing for machine vision. In: ICRA, p. 2791 (2001)Google Scholar
  18. 18.
    Otsu N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cyber. 9, 6266 (1979)Google Scholar
  19. 19.
    Song, Y., Sun, L.: A new auto focusing algorithm for optical microscopebased automated system. In: ICARCV (2006)Google Scholar
  20. 20.
    Sun, Y., Duthaler, S., Nelson, B.: Autofocusing algorithm selection in computer microscopy. In: Proceedings of the IEEE Conference on IROS (2005)Google Scholar
  21. 21.
    Tahri, O., Chaumette, F.: Image moments: generic descriptors for decoupled image-based visual servoing. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 1861–1867 (2004)Google Scholar
  22. 22.
    Tahri, O., Chaumette, F.: Complex objects pose estimation based on image moment invariants. In: Proceedings of the IEEE Conference on Robots and Automation, pp. 436–441 (2005)Google Scholar
  23. 23.
    Walter A., Spies H., Terjung S., Kusters R., Kirchgebner N., Schurr U.: Spatio-temporal dynamics of expansion growth in roots: automatic quantification of diurnal course and temperature response by digital image sequence processing. J. Exp. Biol. 53, 689–698 (2002)Google Scholar
  24. 24.
    Wang L., Uilecan I., Assadi A., Kozmik C., Spalding E.: Hypotrace image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis. Plant Physiol. 149, 1632–1637 (2009)CrossRefGoogle Scholar
  25. 25.
    Yap P., Raveendran P.: Image focus measure based on Chebyshev moments. IEE Proc.-Vis. Image Signal Process. 151(2), 128 (2004)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Ram Subramanian
    • 1
  • Edgar P. Spalding
    • 2
  • Nicola J. Ferrier
    • 1
  1. 1.Department of Mechanical EngineeringUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Department of BotanyUniversity of Wisconsin-MadisonMadisonUSA

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