Machine Vision and Applications

, Volume 27, Issue 5, pp 611–624 | Cite as

Generation and application of hyperspectral 3D plant models: methods and challenges

  • Jan Behmann
  • Anne-Katrin Mahlein
  • Stefan Paulus
  • Jan Dupuis
  • Heiner Kuhlmann
  • Erich-Christian Oerke
  • Lutz Plümer
Special Issue Paper

Abstract

Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they also have been used for close-range sensing of plant canopies with a highly complex architecture. However, the complex geometry of plants and their interaction with the illumination setting severely affect the spectral information obtained. Furthermore, the spatial component of analysis results gain in importance as higher plants are represented by multiple plant organs as leaves, stems and seed pods. The combination of hyperspectral images and 3D point clouds is a promising approach to face these problems. 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. We sum up a geometric calibration method for hyperspectral pushbroom cameras using a reference object for the combination of spectral and spatial information. Furthermore, we show exemplarily new calibration and analysis methods enabled by the hyperspectral 3D models in an experiment with sugar beet plants. An improved normalization, a comparison of image and 3D analysis and the density estimation of infected surface points underline some of the new capabilities gained using this new data type. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified and modeled. In future, reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential to improve automated plant phenotyping significantly.

Keywords

Hyperspectral 3D scanning Close range Plant phenotyping Modeling Sensor fusion 

Notes

Acknowledgments

The authors acknowledge the funding of the CROP.SENSe.net project in the context of Ziel 2-Programms NRW 2007–2013 “Regionale Wettbewerbsfähigkeit und Beschäftigung (EFRE)” by the Ministry for Innovation, Science and Research (MIWF) of the state North Rhine Westphalia (NRW) and European Union Funds for regional development (EFRE) (005-1103-0018) during the preparation of the manuscript.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jan Behmann
    • 1
  • Anne-Katrin Mahlein
    • 2
  • Stefan Paulus
    • 3
  • Jan Dupuis
    • 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

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