Generation and Application of Hyperspectral 3D Plant Models

  • Jan Behmann
  • Anne-Katrin Mahlein
  • Stefan Paulus
  • Heiner Kuhlmann
  • Erich-Christian Oerke
  • Lutz Plümer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8928)


Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they have been also used for close-range sensing of plant canopies with a more complex architecture. The complex geometry of plants and their interaction with the illumination scenario severely affect the spectral information obtained. The combination of hyperspectral images and 3D point clouds are a promising approach to face this problem. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified an modeled. Reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential potential to improve automated phenotyping significantly.

We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. The reliable and accurate generation requires the adaptation of methods designed for man-made scenes. The adaption requires new types of point descriptors and 3D matching technologies. Also the application and analysis of 3D plant models creates new challenges as the light scattering at plant tissue is highly complex and so far not fully described. New approaches for measuring, simulating, and visualizing light fluxes are required for improved sensing and new insights into stress reactions of plants.


Hyperspectral 3D scanning Close range Phenotyping Modeling Sensor fusion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material (8.8 mb)
Supplementary material (ZIP 9,005 KB)


  1. 1.
    Fiorani, F., Rascher, U., Jahnke, S., Schurr, U.: Imaging plants dynamics in heterogenic environments. Current Opinion in Biotechnology 23, 227–235 (2012)CrossRefGoogle Scholar
  2. 2.
    Mahlein, A.K., Oerke, E.C., Steiner, U., Dehne, H.W.: Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology 133(1), 197–209 (2012)CrossRefGoogle Scholar
  3. 3.
    Paulus, S., Schumann, H., Leon, J., Kuhlmann, H.: A high precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosystems Engineering 121, 1–11 (2014)CrossRefGoogle Scholar
  4. 4.
    Paulus, S., Behmann, J., Mahlein, A.K., Plümer, L., Kuhlmann, H.: Low-cost 3D systems - well suited tools for plant phenotyping. Sensors 14, 3001–3018 (2014)CrossRefGoogle Scholar
  5. 5.
    Bousquet, L., Lachérade, S., Jacquemoud, S., Moya, I.: Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment 98(2–3), 201–211 (2005)CrossRefGoogle Scholar
  6. 6.
    Comar, A., Baret, F., Viénot, F., Yan, L., de Solan, B.: Wheat leaf bidirectional reflectance measurements: Description and quantification of the volume, specular and hot-spot scattering features. Remote Sensing of Environment 121, 26–35 (2012)CrossRefGoogle Scholar
  7. 7.
    Gupta, R., Hartley, R.I.: Linear pushbroom cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(9), 963–975 (1997)CrossRefGoogle Scholar
  8. 8.
    Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P.: Franois, C., Ustin, S.L.: Prospect + sail models: A review of use for vegetation characterization. Remote Sensing of Environment 113(suppl. 1), S56–S66 (2009)Google Scholar
  9. 9.
    Wagner, B., Santini, S., Ingensand, H., Gärtner, H.: A tool to model 3D coarse-root development with annual resolution. Plant and Soil 346(1–2), 79–96 (2011)CrossRefGoogle Scholar
  10. 10.
    Hosoi, F., Nakabayashi, K., Omasa, K.: 3-d modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors 11(2), 2166–2174 (2011)CrossRefGoogle Scholar
  11. 11.
    Omasa, K., Hosoi, F., Konishi, A.: 3D Lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany 58(4), 881–898 (2007)CrossRefGoogle Scholar
  12. 12.
    Biskup, B., Scharr, H., Schurr, U., Rascher, U.: A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell & Environment 30(10), 1299–308 (2007)CrossRefGoogle Scholar
  13. 13.
    Liang, J., Zia, A., Zhou, J., Sirault, X.: 3d plant modelling via hyperspectral imaging. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 172–177 (2013)Google Scholar
  14. 14.
    Tilly, N., Hoffmeister, D., Liang, H., Cao, Q., Liu, Y., Miao, Y., Bareth, G.: Evaluation of terrestrial laser scanning for rice growth monitoring. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Congress, Melbourne, Australia XXXIX, pp. 351–356 (2012)Google Scholar
  15. 15.
    Bellasio, C., Olejníčková, J., Tesa, R., Sebela, D., Nedbal, L.: Computer reconstruction of plant growth and chlorophyll fluorescence emission in three spatial dimensions. Sensors 12(1), 1052–1071 (2012)CrossRefGoogle Scholar
  16. 16.
    Paulus, S., Dupuis, J., Mahlein, A., Kuhlmann, H.: Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinformatics 14, 238–251 (2013)CrossRefGoogle Scholar
  17. 17.
    Schöler, F., Steinhage, V.: Towards an automated 3D reconstruction of plant architecture. In: Schürr, A., Varró, D., Varró, G. (eds.) AGTIVE 2011. LNCS, vol. 7233, pp. 51–64. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  18. 18.
    Haralick, B.M., Lee, C.N., Ottenberg, K., Nölle, M.: Review and analysis of solutions of the three point perspective pose estimation problem. International Journal of Computer Vision 13(3), 331–356 (1994)CrossRefGoogle Scholar
  19. 19.
    Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L.: Prospect+ sail models: A review of use for vegetation characterization. Remote Sensing of Environment 113, S56–S66 (2009)CrossRefGoogle Scholar
  20. 20.
    Kuester, T., Spengler, D., Barczi, J.F., Segl, K., Hostert, P., Kaufmann, H.: Simulation of multitemporal and hyperspectral vegetation canopy bidirectional reflectance using detailed virtual 3-d canopy models. Geoscience and Remote Sensing 52(4) (2013)Google Scholar
  21. 21.
    Behmann, J., Steinrücken, J., Plümer, L.: Detection of early plant stress responses in hyperspectral images. ISPRS Journal of Photogrammetry and Remote Sensing 93, 98–111 (2014)CrossRefGoogle Scholar
  22. 22.
    Vos, J., Evers, J., Buck-Sorlin, G., Andrieu, B., Chelle, M., De Visser, P.: Functional-structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany 61(8), 2101–2115 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jan Behmann
    • 1
  • Anne-Katrin Mahlein
    • 2
  • Stefan Paulus
    • 3
  • Heiner Kuhlmann
    • 3
  • Erich-Christian Oerke
    • 2
  • Lutz Plümer
    • 1
  1. 1.Institute of Geodesy and Geoinformation (IGG), GeoinformationUniversity of BonnBonnGermany
  2. 2.Institute for Crop Science and Resource Conservation (INRES) - PhytomedicineUniversity of BonnBonnGermany
  3. 3.Institute of Geodesy and Geoinformation (IGG), GeodesyUniversity of BonnBonnGermany

Personalised recommendations