The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

中文翻译：

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使用数字和高光谱成像衍生的生物标记物进行基因型氮反应的高通量表型分析。

开发具有更高氮利用率的农作物品种对于可持续农作物生产至关重要。高通量基因型和表型的结合将加速发现新型等位基因，用于育种具有较高氮利用效率的作物品种。数字和高光谱成像技术可以通过量化冠层反射响应来有效评估植物种群的生长，生物物理和生化性能。在这里，这些技术被用来得出指示生物标记物，生物量和叶绿素水平的自动表型，对应于不同的氮水平。提供了数字和高光谱成像的详细说明以及相关的挑战和必要的考虑因素，并用于描述小麦的氮响应。提出了用于光谱校准和校正，植物面积检测以及植被指数分析的推导的计算方法。我们开发了一种新型的植被指数，以更高的精度估算叶绿素水平，并以有效去除背景光谱的图像处理算法为基础。得出了数字化的枝条生物量和生长参数，从而能够在营养期对小麦植物进行有效的表型分析，从而无需进行表型分析直至成熟。总体而言，我们的结果表明，在高通量数字和光谱表象学的集成中，对快速筛查大量小麦群体的氮响应具有价值。我们开发了一种新型的植被指数，以更高的精度估算叶绿素水平，并以有效去除背景光谱的图像处理算法为基础。得出了数字化的枝条生物量和生长参数，从而能够在营养期对小麦植物进行有效的表型分析，从而无需进行表型分析直至成熟。总体而言，我们的结果表明，在高通量数字和光谱表象学的集成中，对快速筛查大量小麦群体的氮响应具有价值。我们开发了一种新型的植被指数，以更高的精度估算叶绿素水平，并以有效去除背景光谱的图像处理算法为基础。得出了数字化的枝条生物量和生长参数，从而能够在营养期对小麦植物进行有效的表型分析，从而无需进行表型分析直至成熟。总体而言，我们的结果表明，在高通量数字和光谱表象学的集成中，对快速筛查大量小麦群体的氮响应具有价值。