Skip to main content

A framework for the extraction of quantitative traits from 2D images of mature Arabidopsis thaliana


In this work, we propose an image-based phenotyping framework for the determination of quantitative traits from mature Arabidopsis thaliana plants. Two-dimensional (2D) images taken from the dried and flattened plants are analyzed regarding their geometry as well as their branching topology. The realistic branching architecture is hereby reconstructed from a single 2D image using a tracing approach with a semi-circular search window. The centerline segments of the tracing procedure are subsequently merged and labeled based on a hierarchical approach combining continuity properties with geometrical and topological information determined during tracing. This paper covers a detailed description of the proposed plant phenotyping pipeline from the image acquisition process until the extraction of the quantitative traits. The framework is evaluated using a set of 106 images and compared to a manual phenotyping approach as well as a semi-automatic image-based approach. The most relevant results of this evaluation are presented.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10




  1. Al-Tam, F., Adam, H., Anjos, A., Lorieux, M., Larmande, P., Ghesquiere, A., Jouannic, S., Shahbazkia, H.: P-TRAP: a panicle trait phenotyping tool. BMC Plant Biol. 13(1), 122 (2013)

    Article  Google Scholar 

  2. Armengaud, P., Zambaux, K., Hills, A., Sulpice, R., Pattison, R.J., Blatt, M.R., Amtmann, A.: EZ-Rhizo: integrated software for the fast and accurate measurement of root system architecture. Plant J. 57(5), 945–956 (2009)

    Article  Google Scholar 

  3. Arvidsson, S., Perez-Rodriguez, P., Mueller-Roeber, B.: A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol. 191(3), 895–907 (2011)

    Article  Google Scholar 

  4. Augustin, M., Haxhimusa, Y., Busch, W., Kropatsch, W.G.: Image-based phenotyping of the mature Arabidopsis shoot system. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8928, pp. 231–246. Springer, Berlin (2015)

  5. Basu, P., Pal, A., Lynch, J.P., Brown, K.M.: A novel image-analysis technique for kinematic study of growth and curvature. Plant Physiol. 145(2), 305–316 (2007)

    Article  Google Scholar 

  6. Benmansour, F., Fua, P., Türetken, E.: Automated reconstruction of tree structures using path classifiers and mixed integer programming. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 566–573 (2012)

  7. Boroujeni, F.Z., Wirza, R., Rahmat, O., Mustapha, N., Affendey, L.S., Maskon, O.: Automatic selection of initial points for exploratory vessel tracing in fluoroscopic images. Def. Sci. J. 61, 443–451 (2011)

    Google Scholar 

  8. Boroujeni, F.Z., Rahmat, O., Wirza, R., Mustapha, N., Affendey, L.S., Maskon, O.: Coronary artery center-line extraction using second order local features. Comput. Math. Methods Med. 2012 (2012). doi:10.1155/2012/940981

  9. Brachi, B., Morris, G.P., Borevitz, J.O.: Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol. 12(10), 232 (2011)

    Article  Google Scholar 

  10. Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)

    Article  Google Scholar 

  11. Cobb, J.N., DeClerck, G., Greenberg, A., Clark, R., McCouch, S.: Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 126(4), 867–887 (2013)

    Article  Google Scholar 

  12. Crowell, S., Falcão, A.X., Shah, A., Wilson, Z., Greenberg, A.J., McCouch, S.R.: High-resolution inflorescence phenotyping using a novel image-analysis pipeline, panorama. Plant Physiol. 165(2), 479–495 (2014)

  13. Delibasis, K.K., Kechriniotis, A.I., Tsonos, C., Assimakis, N.: Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput. Methods Programs Biomed. 100(2), 108–122 (2010)

    Article  Google Scholar 

  14. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images—a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012)

    Article  Google Scholar 

  15. French, A.P., Ubeda-Tomas, S., Holman, T., Bennett, M., Pridmore, T.: High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiol. 150(4), 1784–1795 (2009)

    Article  Google Scholar 

  16. Godin, C., Costes, E., Sinoquet, H.: A method for describing plant architecture which integrates topology and geometry. Ann. Botany 84(3), 343–357 (1999)

    Article  Google Scholar 

  17. Huang, Y., Zhang, J., Huang, Y.: An automated computational framework for retinal vascular network labeling and branching order analysis. Microvascu. Res. 84(2), 169–177 (2012)

    Article  Google Scholar 

  18. Humplík, J.F., Lazár, D., Fürst, T., Husičková, A., Hýbl, M., Spíchal, L.: Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea ( Pisum sativum L.). Plant Methods 11(1), 1–11 (2015)

    Article  Google Scholar 

  19. The Arabidopsis Genome Initiative: Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814), 796–815 (2000)

  20. Klodt, M., Cremers, D.: High-resolution plant shape measurements from multi-view stereo reconstruction. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8928, pp. 174–184. Springer, Berlin (2015)

  21. Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078–20111 (2014)

    Article  Google Scholar 

  22. Lin, K.S., Tsai, C.L., Tsai, C.H., Sofka, M., Chen, S.J., Lin, W.Y.: Retinal vascular tree reconstruction with anatomical realism. IEEE Trans. Biomed. Eng. 59(12), 3337–3347 (2012)

    Article  Google Scholar 

  23. Lobet, G., Draye, X., Périlleux, C.: An online database for plant image analysis software tools. Plant Methods 9(38), 1–7 (2013)

    Article  Google Scholar 

  24. Longair, M.H., Baker, D.A., Armstrong, J.D.: Simple neurite tracer: Open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27(17), 2453–2454 (2011)

  25. Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Bharath, A.A., Parker, K.H.: Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 90–97 (1999)

  26. Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Chapman, N., Bharath, A.A., Parker, K.H.: Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans. Biomed. Eng. 49(8), 912–917 (2002)

    Article  Google Scholar 

  27. Meijering, E.: Neuron tracing in perspective. Cytom. Part A 77A(7), 693–704 (2010)

    MathSciNet  Article  Google Scholar 

  28. Minervini, M., Abdelsamea, M.M., Tsaftaris, S.A.: Image-based plant phenotyping with incremental learning and active contours. Ecol. Inform. 23, 35–48 (2014)

    Article  Google Scholar 

  29. Minervini, M., Giuffrida, M.V., Tsaftaris, S.: An interactive tool for semi-automated leaf annotation. In: Tsaftaris, S.A., Scharr, H., Pridmore, T. (eds.) Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP), pp. 6.1-6.13. BMVA Press (2015)

  30. Müller-Linow, M., Pinto-Espinosa, F., Scharr, H., Rascher, U.: The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool. Plant Methods 11(1) (2015). doi:10.1186/s13007-015-0052-z

  31. Mutka, A., Bart, R.: Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5, 734 (2015). doi:10.3389/fpls.2014.00734

    Article  Google Scholar 

  32. Naeem, A., French, A.P., Wells, D.M., Pridmore, T.: High-throughput feature counting and measurement of roots. Bioinformatics 27(9), 1337–1338 (2011)

  33. Nguyen, U.T.V., Bhuiyan, A., Park, L.A.F., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46(3), 703–715 (2013)

    Article  Google Scholar 

  34. Pape, J.M., Klukas, C.: 3-D histogram-based segmentation and leaf detection for rosette plants. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8928, pp. 61–74. Springer, Berlin (2015)

  35. Pound, M.P., French, A.P., Murchie, E.H., Pridmore, T.P.: Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 166(4), 1688–1698 (2014)

    Article  Google Scholar 

  36. Robben, D., Türetken, E., Sunaert, S.: Simultaneous segmentation and anatomical labeling of the cerebral vasculature. In: International Conference on Medical Image Computing and Computer Assisted Intervention (2014)

  37. Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. Wiley, New York (1987)

    Book  MATH  Google Scholar 

  38. Rousseeuw, P.J., Driessen, K.V.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3), 212–223 (1999)

    Article  Google Scholar 

  39. Rousseau, D., Chéné, Y., Belin, E., Semaan, G., Trigui, G., Boudehri, K., Franconi, F., Chapeau-Blondeau, F.: Multiscale imaging of plants: current approaches and challenges. Plant Methods 11(1), 1–9 (2015)

    Article  Google Scholar 

  40. Slovak, R., Göschl, C., Su, X., Shimotani, K., Shiina, T., Busch, W.: A scalable open-source pipeline for large-scale root phenotyping of Arabidopsis. Plant Cell 26(6), 2390–2403 (2014)

  41. Sozzani, R., Benfey, P.: High-throughput phenotyping of multicellular organisms: finding the link between genotype and phenotype. Genome Biol. 12(3), 219–225 (2011)

    Article  Google Scholar 

  42. Spalding, E.P., Miller, N.D.: Image analysis is driving a renaissance in growth measurement. Curr. Opin. Plant Biol. 16(1), 100–104 (2013)

    Article  Google Scholar 

  43. Subramanian, R., Spalding, E.P., Ferrier, N.J.: A high throughput robot system for machine vision based plant phenotype studies. Mach. Vis. Appl. 24(3), 619–636 (2013)

    Article  Google Scholar 

  44. Sun, Y.: Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Trans. Med. Imaging 8(1), 78–88 (1989)

    Article  Google Scholar 

  45. Türetken, E., Gonzalez, G., Blum, C., Fua, P.: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9(2–3), 279–302 (2011)

    Article  Google Scholar 

  46. Walter, A., Liebisch, F., Hund, A.: Plant phenotyping: from bean weighing to image analysis. Plant Methods 11(1) (2015). doi:10.1186/s13007-015-0056-8

  47. Weigel, D.: Natural variation in arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 158(1), 2–22 (2012)

    MathSciNet  Article  Google Scholar 

  48. Yin, Y., Adel, M., Bourennane, S.: Retinal vessel segmentation using a probabilistic tracking method. Pattern Recogn. 45(4), 1235–1244 (2012)

    Article  MATH  Google Scholar 

  49. Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. Trans. Image Process. 10(7), 1010–1019 (2001)

    Article  MATH  Google Scholar 

  50. Zhang, Y., Zhou, X., Degterev, A., Lipinski, M., Adjeroh, D., Yuan, J., Wong, S.: A novel tracing algorithm for high throughput imaging screening of neuron-based assays. J. Neurosci. Methods 160(1), 149–62 (2007)

    Article  Google Scholar 

  51. Zheng, Y., Gu, S., Edelsbrunner, H., Tomasi, C., Benfey, P.: Detailed reconstruction of 3D plant root shape. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2026–2033 (2011)

Download references


We want to thank Svante Holm (Mid Sweden University, SE) and Alison Anastasio (University of Chicago, US) for planting and harvesting the plants, Man Yu and Andrew Davis for taking the photos and Benjamin Brachi (Bergelson Lab, University of Chicago, US) for his valuable inputs and support along the stages of development.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Marco Augustin or Yll Haxhimusa.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Augustin, M., Haxhimusa, Y., Busch, W. et al. A framework for the extraction of quantitative traits from 2D images of mature Arabidopsis thaliana . Machine Vision and Applications 27, 647–661 (2016).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Image-based phenotyping
  • Geometrical traits
  • Topological traits
  • Tracing
  • Hierarchical reconstruction
  • Network of curvilinear structures