Skip to main content
SpringerLink
Log in

Shape descriptors to characterize the shoot of entire plant from multiple side views of a motorized depth sensor

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

A low-cost depth camera recently introduced is synchronized with a specially devised low-cost motorized turntable. This results in a low-cost motorized depth sensor, able to provide a large number of registered side views, which is exploited here for the quantitative characterization of the shoots of entire plants. A set of four new shape descriptors of the shoots, constructed from the depth images on multiple side views of the shoots of plants, is proposed. The four descriptors quantify effective volume, multiscale organization, spatial symmetries and lacunarity of the plants. The four descriptors are here defined, validated on synthetic scenes with known properties, and then applied on nine different-looking real plants to illustrate their abilities for quantitative characterization and comparison. The resulting motorized depth sensor and associated image processing open new perspectives to various plant science applications including plant growth and architecture monitoring, plant response to stresses or the assessment of aesthetic parameters for ornamental plants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Fiorani, F., Rascher, U., Jahnke, S.: Imaging plants dynamics in heterogenic environments. Curr. Opin. Biotechnol. 23, 227–235 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Hiremath, S., Tolpekin, V.A., van der Heijden, G., Stein, A.: Segmentation of Rumex obtusifolius using Gaussian Markov random fields. Mach. Vis. Appl. 24, 845–845 (2013)

    Article  Google Scholar 

  4. Zeng, G., Birchfield, S.T., Wells, C.E.: Rapid automated detection of roots in minirhizotron images. Mach. Vis. Appl. 21, 309–317 (2013)

    Article  Google Scholar 

  5. van der Heijden, G., Song, Y., Horgan, G., Polder, G., Dieleman, A., Bink, M., Palloix, A., van Eeuwijk, F., Glasbey, C.: SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Funct. Plant Biol. 39, 870–877 (2012)

    Article  Google Scholar 

  6. Bellasio, C., Olejníčková, J., Tesař, R., Šebela, D., Nedbal, L.: Computer reconstruction of plant growth and chlorophyll fluorescence emission in three spatial dimensions. Sensors 12, 1052–1071 (2012)

    Article  Google Scholar 

  7. Azzary, G., Goulden, M.L., Rusu, R.B.: Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors 13, 2384–2398 (2013)

    Article  Google Scholar 

  8. Biskup, B., Scharr, H., Schurr, U., Rascher, U.: A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ. 10, 1299–1308 (2007)

    Article  Google Scholar 

  9. Omasa, K., Hosoi, F., Konishi, A.: 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 158, 881–898 (2007)

    Google Scholar 

  10. Bucksch, A., Fleck, S.: Automated detection of branch dimensions in woody skeletons of fruit tree canopies. Photogramm. Eng. Remote Sens. 77, 229–240 (2011)

    Article  Google Scholar 

  11. Klose, R., Penlington, J., Ruckelshausen, A.: Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Bornimer Agrartechnische Berichte 69, 93–105 (2009)

    Google Scholar 

  12. Kraft, M., Saloma De Freitag, N., Munack, A.: Test of a 3D time of flight camera for shape measurements of plants. In: CIGR Workshop on Image Analysis in Agriculture, Budapest, Hungary, 26–27 Aug 2010 (2010)

  13. Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., Belin, E., Chapeau-Blondeau, F.: On the use of depth camera for 3D phenotyping of entire plants. Comput. Electron. Agric. 82, 122–127 (2012)

    Article  Google Scholar 

  14. Chéné, Y., Belin, E., Chapeau-Blondeau, F, Boureau, T., Caffier, V., Rousseau, D.: Anatomo-functional bimodality imaging for plant phenotyping: An insight through depth imaging coupled to thermal imaging. Chap. 9, in Dutta Gupta, S., Ibaraki, Y., (eds.) Plant Image Analysis: Fundamentals and Applications. CRC Press, Boca Raton (2015)

  15. Chéné, Y., Belin, E., Rousseau, D., Chapeau-Blondeau, F.: Multiscale analysis of depth images from natural scenes: scaling in the depth of the woods. Chaos Solitons Fractals 54, 135–149 (2013)

  16. Golbach, F., Kootstra, G., Damjanovic, S., Otten, G., van de Zedde, R.: Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping. Mach. Vis. Appl. 1–18 (2015)

  17. Nguyen, T.T., Slaughter, D.C., Max, N., Maloof, J.N., Sinha, N.: Structured light-based 3D reconstruction system for plants. Sensors 15(8), 18587–18612 (2015)

    Article  Google Scholar 

  18. Sansoni, G., Trebeschi, M., Docchio, F.: State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors 9, 568–601 (2009)

    Article  Google Scholar 

  19. Garbez, M., Chéné, Y., Belin, É., Sigogne, M., Labatte, J.L., Hunault, G., Rousseau, D., Galopin, G.: Predicting sensorial attribute scores of ornamental plants assessed in 3D through rotation on video by image analysis: a study on the morphology of virtual rose bushes. Comput. Electron. Agric. 121, 331–346 (2016)

    Article  Google Scholar 

  20. Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12, 1437–1454 (2012)

    Article  Google Scholar 

  21. http://labjack.com/u3

  22. http://qt.digia.com/

  23. http://www.openni.org/

  24. Schnabel, R., Wahl, R., Klein, R.: Efficient RANSAC for point cloud shape detection. Comput. Graph. Forum 26, 214–226 (2007). (Blackwell Publishing Ltd)

    Article  Google Scholar 

  25. Chauveau, J., Rousseau, D., Richard, P., Chapeau-Blondeau, F.: Fractal structure in the color distribution of natural images. Chaos Solitons Fractals 42, 472–482 (2009)

    Article  Google Scholar 

  26. Chauveau, J., Rousseau, D., Richard, P., Chapeau-Blondeau, F.: Multifractal analysis of three-dimensional histogram from color images. Chaos Solitons Fractals 43, 57–67 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  27. Chauveau, J., Rousseau, D., Chapeau-Blondeau, F.: Fractal capacity dimension of three-dimensional histogram from color images. Multidimens. Syst. Signal Process. 21, 197–211 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  28. Venus head point cloud. http://www.dirdim.com (2014)

  29. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975)

    Google Scholar 

  30. Da Silva, D., Boudon, F., Godin, C., Sinoquet, H.: Multiscale framework for modeling and analyzing light interception by trees. Multiscale Model. Simul. 7, 910–933 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  31. Paulus, S., Dupuis, J., Mahlein, A.K., Kuhlmann, H.: Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform. 14, 238–249 (2013)

    Article  Google Scholar 

  32. Paulus, S., Schumann, H., Kuhlmann, H., Léon, J.: High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst. Eng. 121, 1–11 (2014)

    Article  Google Scholar 

  33. Paulus, S., Dupuis, J., Riedel, S., Kuhlmann, H.: Automated analysis of barley organs using 3D laser scanning: an approach for high throughput phenotyping. Sensors 14, 12670–12686 (2014)

    Article  Google Scholar 

  34. http://lisabiblio.univ-angers.fr/PHENOTIC/Shape_descriptors.zip

  35. Boumaza, R., Huché-Thélier, L., Demotes-Mainard, S., Coz, E.L., Leduc, N., Pelleschi-Travier, S., Qannari, E.M., Sakr, S., Santagostini, P., Symoneaux, R., Gurin, V.: Sensory profiles and preference analysis in ornamental horticulture: the case of the rosebush. Food Qual. Prefer. 21, 987–997 (2010)

  36. Garbez, M., Galopin, G., Sigogne, M., Favre, P., Demotes-Mainard, S., Symoneaux, R.: Assessing the visual aspect of rotating virtual rose bushes by a labeled sorting task. Food Qual. Prefer. 40, 287–295 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

Yann Chéné acknowledges support from Région Pays de la Loire, France, for the funding of his Ph.D.

Author information

Authors and Affiliations

  1. Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, 62 avenue Notre Dame du Lac, 49000, Angers, France

    Yann Chéné, Étienne Belin & François Chapeau-Blondeau

  2. Université de Lyon, CREATIS, CNRS UMR 5220, INSERM U1044, Université Lyon 1, INSA-Lyon, 69621, Villeurbanne, France

    David Rousseau

  3. Agrocampus Ouest, Centre d’Angers-Institut National d’Horticulture et du Paysage, UMR 1345 IRHS (Agrocampus Ouest, INRA, Université d’Angers), 2 rue Le Nôtre, 49045, Angers Cedex 01, France

    Morgan Garbez & Gilles Galopin

Authors
  1. Yann Chéné
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Rousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Étienne Belin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Morgan Garbez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gilles Galopin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. François Chapeau-Blondeau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Étienne Belin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chéné, Y., Rousseau, D., Belin, É. et al. Shape descriptors to characterize the shoot of entire plant from multiple side views of a motorized depth sensor. Machine Vision and Applications 27, 447–461 (2016). https://doi.org/10.1007/s00138-016-0762-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-016-0762-x

Keywords

Search

Navigation