Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.

ABSTRACT: Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results in

SUBMITTER: Wiegmann M 

PROVIDER: S-EPMC6837513 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature