Seedscreener: A novel integrated wheat germplasm phenotyping platform based on NIR-feature detection and 3D-reconstruction

Abstract

Crop seed phenomics provides a new operational basis for breeders. However, a scarcity of seed phenotypic characterization, especially of the single seed kernels, severely restricts their use in plant breeding. In this study, we focus on designing and deploying a prototype for automatic and high throughput seed phenotyping. The novel setup, Seedscreener, allows simultaneous RGB imaging and NIR spectrum analysis pipeline to evaluate single kernel 3D morphologies and internal biochemicals. With the assistance of a data acquisition device centered on a near-infrared spectrometer and an industrial camera, as well as an automated acquisition software under the QT development framework, Seedscreener has the ability to acquire both endophenotype and exophenotype traits of single wheat kernels simultaneously. The developed biochemical endophenotype traits prediction model can forecast the protein content (PC), starch content (SC) and gluten content (GC) of wheat kernel by the full-band absorbance spectra. The developed morphological exophenotype traits extraction model based on Marching Cubes (MC) algorithm can calculate the wheat grain length (GL), grain width (GW), grain thickness (GT), grain surface area (GSA) and convex hull volume (CHV) using the three-dimensional visual hull constructed by the lateral profile information of the wheat grain. The results show that the platform could reach 94% success rate of single wheat phenotypic data collection, the average accuracy of endophenotype traits prediction is 0.85, the average accuracy of exophenotype traits extraction is 0.93, and the working flux of the platform is about 90 grains/h. Compared with existing individual wheat kernels phenotyping equipment, Seedscreener could obtain both endophenotype and exophenotype characteristics of wheat seeds with high throughput and precision.