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Automatic estimation of wheat grain morphometry from computed tomography data.

Harry Strange1, Reyer Zwiggelaar1, Craig Sturrock2

  • 1Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK.

Functional Plant Biology : FPB
|June 3, 2020
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Summary

This study introduces an automated image analysis pipeline to measure wheat grain size and shape. Distinct differences in grain volume and crease depth were found between commercial and landrace wheat varieties.

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Area of Science:

  • Agricultural Science
  • Image Analysis
  • Plant Morphology

Background:

  • Wheat grain size and morphology are critical agronomic traits influencing yield and quality.
  • Distinguishing between commercial and landrace wheat varieties based on grain characteristics is important for breeding and crop improvement.

Purpose of the Study:

  • To develop and validate a fully automated, data-driven image processing pipeline for extracting individual wheat grains from spikes.
  • To quantify and compare morphometric measures of individual grains from commercial and landrace wheat types.

Main Methods:

  • Computed tomography (CT) imaging was used to capture whole wheat spikes.
  • An image processing pipeline involving adaptive thresholding, morphological filtering, and volumetric reconstruction was employed for automated grain extraction.
  • Morphometric parameters, including volume and crease depth, were extracted from individual grains.

Main Results:

  • The automated pipeline successfully extracted individual grains and their morphometric data.
  • Significant differences were observed between the commercial (Capelle) and landrace (Indian Shot Wheat) types.
  • Commercial wheat exhibited significantly greater average volume (P=0.0024) and crease depth (P=1.61×10-5) compared to the landrace.

Conclusions:

  • The developed automated approach effectively retains developmental information and reveals novel insights into individual wheat grain morphology.
  • This method provides a powerful tool for quantitative analysis of wheat grain traits, aiding in crop characterization and breeding efforts.