Automatic estimation of wheat grain morphometry from computed tomography data

Harry Strange, Reyer Zwiggelaar, Craig Sturrock, Sacha Mooney & John Doonan

Wheat (Triticum aestivum L.) grain size and morphology are playing an increasingly important role as agronomic traits. Whole spikes from two disparate strains, the commercial type Capelle and the landrace Indian Shot Wheat, were imaged using a commercial computed tomography system. Volumetric information was obtained using a standard back-propagation approach. To extract individual grains within the spikes, we used an image processing pipeline that included adaptive thresholding, morphological filtering, persistence aspects and volumetric reconstruction. This is a fully automated, data-driven pipeline. Subsequently, we extracted several morphometric measures from the individual grains. Taking the location and morphology of the grains into account, we show distinct differences between the commercial and landrace types. For example, average volume is significantly greater for the commercial type (P = 0.0024), as is the crease depth (P = 1.61 × 10-5). This pilot study shows that the fully automated approach described can retain developmental information and reveal new morphology information at an individual grain level.

In press in Functional Plant Biology