Abstract
AbstractClimate change leads to increased risks of reduced Hagberg falling numbers (HFN). The objectives of this study were to (i) compare partial least square (PLS) with deep learning methods with respect to establishing near‐infrared spectroscopy (NIRS) calibrations for HFN in spring barley, (ii) compare the accuracy of NIRS calibrations for metric versus categorical response variables and (iii) discuss the usefulness of the developed NIRS calibrations in the context of barley breeding programmes. This study was based on 560 samples from the preapplication trial of a commercial spring barley breeding programme and the double round robin population of barley. Across all the examined preprocessing combinations, the lowest root mean square error of prediction (RMSEP) in the calibration set was observed with 59.95 s for PLS regression where that of the best deep learning method was higher. The accuracies to predict HFN from NIR spectra observed in our study indicated that they cannot replace the HFN reference method, but their use for selection in early generations of barley breeding programmes are promising.
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