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Review
. 2022 Apr 8:13:837200.
doi: 10.3389/fpls.2022.837200. eCollection 2022.

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

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Review

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

Junjie Ma et al. Front Plant Sci. .

Abstract

Recent research advances in wheat have focused not only on increasing grain yields, but also on establishing higher grain quality. Wheat quality is primarily determined by the grain protein content (GPC) and composition, and both of these are affected by nitrogen (N) levels in the plant as it develops during the growing season. Hyperspectral remote sensing is gradually becoming recognized as an economical alternative to traditional destructive field sampling methods and laboratory testing as a means of determining the N status within wheat. Currently, hyperspectral vegetation indices (VIs) and linear nonparametric regression are the primary tools for monitoring the N status of wheat. Machine learning algorithms have been increasingly applied to model the nonlinear relationship between spectral data and wheat N status. This study is a comprehensive review of available N-related hyperspectral VIs and aims to inform the selection of VIs under field conditions. The combination of feature mining and machine learning algorithms is discussed as an application of hyperspectral imaging systems. We discuss the major challenges and future directions for evaluating and assessing wheat N status. Finally, we suggest that the underlying mechanism of protein formation in wheat grains as determined by using hyperspectral imaging systems needs to be further investigated. This overview provides theoretical and technical support to promote applications of hyperspectral imaging systems in wheat N status assessments; in addition, it can be applied to help monitor and evaluate food and nutrition security.

Keywords: grain protein; hyperspectral imaging; machine learning; vegetation index; wheat.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
It is a review of hyperspectral imaging systems for evaluating wheat grain protein. Hyperspectral imaging systems, a combination of hyperspectral remote sensing and machine learning, have significant advantages in evaluating wheat grain proteins. Hyperspectral remote sensing can capture information reflecting nitrogen (N) status in wheat plants in real-time and non-destructively. Meanwhile, machine learning can effectively simulate the non-linear relationship between nitrogen and spectral data of wheat. Hyperspectral imaging systems are now widely used to predict wheat grain protein content (GPC), and crop models can complement the analysis of eco-physiological mechanisms in the prediction process.

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References

    1. Asseng S., Martre P., Maiorano A., Rotter R. P., Oleary G., Fitzgerald G. J., et al. (2019). Climate change impact and adaptation for wheat protein. Glob. Change Biol. 25 155–173. 10.1111/gcb.14481 - DOI - PubMed
    1. Asseng S., Milroy S. P. (2006). Simulation of environmental and genetic effects on grain protein concentration in wheat. Eur. J. Agron. 25 119–128. 10.1016/j.eja.2006.04.005 - DOI
    1. Babar M. A., Reynolds M. P., van Ginkel M., Klatt A. R., Raun W. R., Stone M. L. (2006). Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 46 1046–1057. 10.2135/cropsci2005.0211 - DOI - PubMed
    1. Baret F., Houlès V., Guérif M. (2007). Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. J. Exp. Bot. 58 869–880. 10.1093/jxb/erl231 - DOI - PubMed
    1. Barnes E., Clarke T. R., Richards S. E., Colaizzi P., Haberland J., Kostrzewski M., et al. (2000). “Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data,” in Proceedings of the 2000 5th International Conference on Precision Agriculture, Bloomington, MN, 1–15. 10.1094/cm-2009-1211-01-rs - DOI

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