Ma Junjie, Zheng Bangyou, He Yong
Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China.
CSIRO Agriculture and Food, Queensland Bioscience Precinct, St. Lucia, QLD, Australia.
Front Plant Sci. 2022 Apr 8;13:837200. doi: 10.3389/fpls.2022.837200. eCollection 2022.
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.
近年来，小麦研究进展不仅聚焦于提高籽粒产量，还致力于提升籽粒品质。小麦品质主要由籽粒蛋白质含量（GPC）及其组成决定，而在生长季植株生长过程中，这两者均受植株体内氮（N）水平的影响。高光谱遥感作为一种测定小麦氮素状况的手段，正逐渐被视为传统破坏性田间采样方法和实验室检测的经济替代方法。目前，高光谱植被指数（VIs）和线性非参数回归是监测小麦氮素状况的主要工具。机器学习算法已越来越多地应用于模拟光谱数据与小麦氮素状况之间的非线性关系。本研究全面综述了现有的与氮相关的高光谱植被指数，旨在为田间条件下植被指数的选择提供参考。讨论了特征挖掘与机器学习算法相结合在高光谱成像系统中的应用。我们探讨了评估和评价小麦氮素状况的主要挑战及未来方向。最后，我们建议利用高光谱成像系统确定小麦籽粒蛋白质形成的潜在机制仍需进一步研究。本综述为推动高光谱成像系统在小麦氮素状况评估中的应用提供了理论和技术支持；此外，它还可用于帮助监测和评估粮食与营养安全。
