Advertisement

Distortion Correction in 3D-Modeling of Root Systems for Plant Phenotyping

  • Tushar Kanta Das Nakini
  • Guilherme N. DeSouzaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8928)

Abstract

Root Phenotyping is an important tool in predicting the life and growth of plants. Many systems have been developed to automate the process of extracting root traits using 3D imaging system, however, not many of those systems corrected for the distortions that frequently appear during this process. In this paper we present a new method to compensate for light refractions that occur due to hydroponic substrates – gel-based platforms for growing plants. As our results demonstrate, our method provides an accurate 3D point cloud containing the coordinates of the surface of the root system with error smaller than 0.16 mm in average and standard deviation of less than 0.13 mm.

Keywords

Root phenotyping Gel-based media Hydroponicsubstrate Glass cylinder Distortion correction 3D modeling 

References

  1. 1.
    Agin, G., Binford, T.: Computer Description of Curved Objects. IEEE Transactions on Computers C-25(4), 439–449 (1976)Google Scholar
  2. 2.
    Besl, P.J.: Active optical range imaging sensors. In: Advances in Machine Vision, pp. 1–63. Springer-Verlag New York Inc., New York (1988). http://dl.acm.org/citation.cfm?id=57360.57361
  3. 3.
    Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  4. 4.
    Bidel, L.P.R., Pagès, L., Rivière, L.M., Pelloux, G., Lorendeau, J.Y.: Massflowdyn i: A carbon transport and partitioning model for root system architecture. Annals of Botany 85(6), 869–886 (2000). http://aob.oxfordjournals.org/content/85/6/869.abstract CrossRefGoogle Scholar
  5. 5.
    Bowman, D., Devitt, D., Engelke, M., Rufty Jr., T.: Root architecture affects nitrate leaching from bentgrass turf. Crop Science 38(6), 1633–1639 (1998). http://www.scopus.com/inward/record.url?eid=2-s2.0-0032447708&partnerID=40&md5=aeea90ddd24b67be28c236f5d178d118, cited By (since 1996)48
  6. 6.
    Chen, C.H., Kak, A.: Modeling and calibration of a structured light scanner for 3-D robot vision. In: Proceedings of the 1987 IEEE International Conference on Robotics and Automation, vol. 4, pp. 807–815 (1987)Google Scholar
  7. 7.
    Chen, Y.L., Dunbabin, V.M., Diggle, A.J., Siddique, K.H.M., Rengel, Z.: Development of a novel semi-hydroponic phenotyping system for studying root architecture. Functional Plant Biology 38(5), 355–363 (2011)CrossRefGoogle Scholar
  8. 8.
    Clark, R.T., MacCurdy, R.B., Jung, J.K., Shaff, J.E., McCouch, S.R., Aneshansley, D.J., Kochian, L.V.: Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform. Plant Physiology 156(2), 455–465 (2011). http://dx.doi.org/10.1104/pp.110.169102 CrossRefGoogle Scholar
  9. 9.
    Doussan, C., Pagès, L., Vercambre, G.: Modelling of the hydraulic architecture of root systems: An integrated approach to water absorption - Model description. Annals of Botany 81, 213–223 (1998)CrossRefGoogle Scholar
  10. 10.
    Fang, S., Yan, X., Liao, H.: 3D reconstruction and dynamic modeling of root architecture in situ and its application to crop phosphorus research. The Plant Journal 60(6), 1096–1108 (2009). http://dx.doi.org/10.1111/j.1365-313X.2009.04009.x CrossRefGoogle Scholar
  11. 11.
    French, A., Ubeda-Tomás, S., Holman, T.J., Bennett, M.J., Pridmore, T.: High-Throughput Quantification of Root Growth Using a Novel Image-Analysis Tool. Plant Physiology 150(4), 1784–1795 (2009). http://www.plantphysiol.org/content/150/4/1784.abstract CrossRefGoogle Scholar
  12. 12.
    Fua, P.: Reconstructing complex surfaces from multiple stereo views. In: Proceedings of the Fifth International Conference on Computer Vision, pp. 1078–1085 (1995)Google Scholar
  13. 13.
    Gregory, P., Hutchison, D., Read, D., Jenneson, P., Gilboy, W., Morton, E.: Non-invasive imaging of roots with high resolution X-ray micro-tomography. Plant and Soil 255(1), 351–359 (2003). http://dx.doi.org/10.1023/A:1026179919689 CrossRefGoogle Scholar
  14. 14.
    Heeraman, D., Hopmans, J., Clausnitzer, V.: Three dimensional imaging of plant roots in situ with X-ray Computed Tomography. Plant and Soil 189(2), 167–179 (1997). http://dx.doi.org/10.1023/B:PLSO.0000009694.64377.6f Google Scholar
  15. 15.
    Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A 4(4), 629–642 (1987)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Idesawa, M., Yatagai, T., Soma, T.: Scanning moiré method and automatic measurement of 3-D shapes. Appl. Opt. 16(8), 2152–2162 (1977). http://ao.osa.org/abstract.cfm?URI=ao-16-8-2152 CrossRefGoogle Scholar
  17. 17.
    Iyer-Pascuzzi, A.S., Symonova, O., Mileyko, Y., Hao, Y., Belcher, H., Harer, J., Weitz, J.S., Benfey, P.N.: Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems. Plant Physiology 152(3), 1148–1157 (2010). http://www.plantphysiol.org/content/152/3/1148.abstract CrossRefGoogle Scholar
  18. 18.
    Jahnke, S., Menzel, M.I., Van Dusschoten, D., Roeb, G.W., Bühler, J., Minwuyelet, S., Blümler, P., Temperton, V.M., Hombach, T., Streun, M., Beer, S., Khodaverdi, M., Ziemons, K., Coenen, H.H., Schurr, U.: Combined MRI-PET dissects dynamic changes in plant structures and functions. The Plant Journal 59(4), 634–644 (2009). http://dx.doi.org/10.1111/j.1365-313X.2009.03888.x CrossRefGoogle Scholar
  19. 19.
    Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. IEEE Press, New York (1988)zbMATHGoogle Scholar
  20. 20.
    Kazo, C., Hajder, L.: High-quality structured-light scanning of 3D objects using turntable. In: 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom), pp. 553–557 (2012)Google Scholar
  21. 21.
    Lam, D., Hong, R.Z., DeSouza, G.: 3D human modeling using virtual multi-view stereopsis and object-camera motion estimation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 4294–4299 (2009)Google Scholar
  22. 22.
    Lambers, H., Shane, M.W., Cramer, M.D., Pearse, S.J., Veneklaas, E.J.: Root structure and functioning for efficient acquisition of phosphorus: Matching morphological and physiological traits. Annals of Botany 98(4), 693–713 (2006). http://aob.oxfordjournals.org/content/98/4/693.abstract CrossRefGoogle Scholar
  23. 23.
    Lobet, G., Pagès, L., Draye, X.: A Novel Image-Analysis Toolbox Enabling Quantitative Analysis of Root System Architecture. Plant Physiology 157(1), 29–39 (2011). http://www.plantphysiol.org/content/157/1/29.abstract CrossRefGoogle Scholar
  24. 24.
    Lynch, J.: Root Architecture and Plant Productivity. Plant Physiology 109(1), 7–13 (1995). http://www.plantphysiol.org/content/109/1/7.short MathSciNetGoogle Scholar
  25. 25.
    Martin, W.N., Aggarwal, J.: Volumetric Descriptions of Objects from Multiple Views. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5(2), 150–158 (1983)Google Scholar
  26. 26.
    Nakini, T., DeSouza, G.N., Prince, S.J., Musket, T., Murphy, M.C., T, N.H.: 3d imaging and feature extraction for root phenotyping of soybean. In: PhenoDays Symposium on Imaging & Robotics for the 21st Century. Danforth Plant Science Center, St. Louis (September 2013)Google Scholar
  27. 27.
    Niem, W.: Robust and fast modelling of 3D natural objects from multiple views. In: SPIE Proceedings Image and Video Processing II, vol. 2182 (1994). doi: 10.1117/12/12.171088
  28. 28.
    Park, J., DeSouza, G.N.: Photo-realistic modeling of three dimensional objects using range and reflectance data. In: Innovations in Machine Intelligence and Robot Perception. Springer (2005)Google Scholar
  29. 29.
    Pound, M.P., French, A.P., Atkinson, J., Wells, D.M., Bennett, M.J., Pridmore, T.P.: RootNav: Navigating images of complex root architectures. Plant Physiology (2013). http://www.plantphysiol.org/content/early/2013/06/12/pp.113.221531.abstract
  30. 30.
    Ribaut, J.M., Betran, J., Monneveux, P., Setter, T.: Drought tolerance in maize. In: Bennetzen, J., Hake, S. (eds.) Handbook of Maize: Its Biology, pp. 311–344. Springer, New YorkGoogle Scholar
  31. 31.
    Scheenen, T., Vergeldt, F., Heemskerk, A., Van As, H.: Intact Plant Magnetic Resonance Imaging to Study Dynamics in Long-Distance Sap Flow and Flow-Conducting Surface Area. Plant Physiology 144(2), 1157–1165 (2007). http://www.plantphysiol.org/content/144/2/1157.abstract CrossRefGoogle Scholar
  32. 32.
    Silverberg, J.L., Noar, R.D., Packer, M.S., Harrison, M.J., Henley, C.L., Cohen, I., Gerbode, S.J.: 3d imaging and mechanical modeling of helical buckling in medicago truncatula plant roots. Proceedings of the National Academy of Sciences (2012). http://www.pnas.org/content/early/2012/09/19/1209287109.abstract
  33. 33.
    Topp, C.N., Iyer-Pascuzzi, A.S., Anderson, J.T., Lee, C.R., Zurek, P.R., Symonova, O., Zheng, Y., Bucksch, A., Mileyko, Y., Galkovskyi, T., Moore, B.T., Harer, J., Edelsbrunner, H., Mitchell-Olds, T., Weitz, J.S., Benfey, P.N.: 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proceedings of the National Academy of Sciences 110(18), E1695–E1704 (2013). http://www.pnas.org/content/110/18/E1695.abstract
  34. 34.
    Trachsel, S., Kaeppler, S., Brown, K., Lynch, J.: Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant and Soil 341(1–2), 75–87 (2011). http://dx.doi.org/10.1007/s11104-010-0623-8 CrossRefGoogle Scholar
  35. 35.
    Tracy, S.R., Roberts, J.A., Black, C.R., McNeill, A., Davidson, R., Mooney, S.J.: The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography. Journal of Experimental Botany 61(2), 311–313 (2010). http://jxb.oxfordjournals.org/content/61/2/311.short CrossRefGoogle Scholar
  36. 36.
    Van As, H., Scheenen, T., Vergeldt, F.: MRI of intact plants. Photosynthesis Research 102(2–3), 213–222 (2009). http://dx.doi.org/10.1007/s11120-009-9486-3 Google Scholar
  37. 37.
    van der Weerd, L., Claessens, M.M., Ruttink, T., Vergeldt, F.J., Schaafsma, T.J., Van As, H.: Quantitative NMR microscopy of osmotic stress responses in maize and pearl millet. Journal of Experimental Botany 52(365), 2333–2343 (2001). http://jxb.oxfordjournals.org/content/52/365/2333.abstract CrossRefGoogle Scholar
  38. 38.
    Yamashita, A., Higuchi, H., Kaneko, T., Kawata, Y.: Three dimensional measurement of object’s surface in water using the light stripe projection method. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, ICRA 2004, vol. 3, pp. 2736–2741 (April 2004)Google Scholar
  39. 39.
    Yamashita, A., Hayashimoto, E., Kaneko, T., Kawata, Y.: 3-D measurement of objects in a cylindrical glass water tank with a laser range finder. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1578–1583 (2003)Google Scholar
  40. 40.
    Yazdanbakhsh, N., Fisahn, J.: High throughput phenotyping of root growth dynamics, lateral root formation, root architecture and root hair development enabled by PlaRoM. Functional Plant Biology 36(11), 938–946 (2009)CrossRefGoogle Scholar
  41. 41.
    Zha, H., Morooka, K., Hasegawa, T., Nagata, T.: Active modeling of 3-D objects: planning on the next best pose (NBP) for acquiring range images. In: Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling, pp. 68–75 (1997)Google Scholar
  42. 42.
    Zhu, T., Fang, S., Li, Z., Liu, Y., Liao, H., Yan, X.: Quantitative analysis of 3-dimensional root architecture based on image reconstruction and its application to research on phosphorus uptake in soybean. Chinese Science Bulletin 51(19), 2351–2361 (2006). http://dx.doi.org/10.1007/s11434-006-2130-0 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tushar Kanta Das Nakini
    • 1
  • Guilherme N. DeSouza
    • 1
    Email author
  1. 1.Vision-Guided and Intelligent Robotics (ViGIR) Lab, Electrical and Computer Engineering DepartmentUniversity of MissouriColumbiaUSA

Personalised recommendations