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A Volumetric Segmentation Method for Learning Structural Representations of Plant Roots in 3D X-Ray CT Scans
  • +3
  • Camilo Valdes,
  • Anjali Iyer-Pascuzzi,
  • Valerian Meline,
  • Joelle Claußen,
  • Stefan Gerth,
  • Jennifer Clarke
Camilo Valdes

Corresponding Author:[email protected]

Author Profile
Anjali Iyer-Pascuzzi
Purdue University
Valerian Meline
Netherlands Plant Eco-phenotyping Center (NPEC)
Joelle Claußen
Fraunhofer Institute for Integrated Circuits IIS
Stefan Gerth
Fraunhofer Institute for Integrated Circuits IIS
Jennifer Clarke
University of Nebraska-Lincoln


Critical factors that determine crop yields are located underground, making them difficult to analyze. Traditionally, these factors have been measured by growing plants in clear media and measuring traits with visible imaging. Modern phenomics technologies use one or several imaging modalities to capture traits that reflect plant physiology or performance. Analytical techniques for plant phenomics are a crucial part of approaches to achieving desirable agronomic and biological traits. Advances in sensor technologies have paved the way for faster and more efficient plant phenotyping, with methods adapted from disciplines like high-resolution 3D X-Ray computed tomography (CT). A crucial step in their analysis is segmentation-the identification and classification of the scan's voxels as "root" or "non-root". Unlike roots in transparent mediums, roots in non-transparent mediums are difficult to segment from their surrounding materials as root and non-root voxels have overlapping CT values. The challenge we address is the development of neural-driven approaches for volumetric semantic segmentation of plant roots in 3D CT scans, and discuss subsequent trait extraction methods that enable the quantification of root systems and their traits in several agriculturally
25 Oct 2022Submitted to NAPPN 2023 Abstracts
28 Oct 2022Published in NAPPN 2023 Abstracts