Development of a near‐infrared spectroscopy calibration for Hagberg falling number assessment of barley (Hordeum vulgare): A comparison of methods

Abstract

Climate 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.

Plant phenotyping relevance

大麦のHagberg falling numberという穀粒形質をNIRSから推定する較正法を開発・比較し、精度を検証しているため、表現型取得法が研究の中心である。

Code and data availability

The paper's HFN phenotype and NIR spectral data are not publicly deposited. The DRR data set (HFN measurements and NIR spectra of ~230 barley samples) is available only from the corresponding author upon reasonable request, and the PAT data set is explicitly unavailable due to its commercial breeding origin. No author-

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