Plant Root System Analysis from MRI Images

  • Hannes Schulz
  • Johannes A. Postma
  • Dagmar van Dusschoten
  • Hanno Scharr
  • Sven Behnke
Part of the Communications in Computer and Information Science book series (CCIS, volume 359)


We present a novel method for deriving a structural model of a plant root system from 3D Magnetic Resonance Imaging (MRI) data of soil grown plants and use it for plant root system analysis. The structural model allows calculation of physiologically relevant parameters. Roughly speaking, MRI images show local water content of the investigated sample. The small, local amounts of water in roots require a relatively high resolution, which results in low SNR images. However, the spatial resolution of the MRI images remains coarse relative to the diameter of typical fine roots, causing many gaps in the visible root system. To reconstruct the root structure, we propose a three step approach: 1) detect tubular structures, 2) connect all pixels to the base of the root using Dijkstras algorithm, and 3) prune the tree using two signal strength related thresholds. Dijkstras algorithm determines the shortest path of each voxel to the base of the plant root, weighing the Euclidean distance measure by a multi-scale vesselness measure. As a result, paths running within good root candidates are preferred over paths in bare soil. We test this method using both virtually generated MRI images of Maize and real MRI images of Barley roots. In experiments on synthetic data, we show limitations of our algorithm with regard to resolution and noise levels. In addition we show how to use our reconstruction for root phenotyping on real MRI data of barley roots and snow pea in soil. Extending our conference publication [1], we show how to use the structural model to remove unwanted structures, like underground weeds.


Root modeling Plant phenotyping Roots in soil Maize Barley 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hannes Schulz
    • 1
  • Johannes A. Postma
    • 2
  • Dagmar van Dusschoten
    • 2
  • Hanno Scharr
    • 2
  • Sven Behnke
    • 1
  1. 1.Computer Science VI: Autonomous Intelligent SystemsUniversity BonnBonnGermany
  2. 2.IBG-2: Plant Sciences, Forschungszentrum JülichJülichGermany

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