Visible and near-infrared spectral imaging is a key non-destructive technique for rapid assessment of biophysical traits of plants. A major challenge with close-range spectral imaging of plants is spectral variation arising from illumination effects, which may mask the signals due to physiochemical differences. In the present work, we describe a new scatter correction technique called variable sorting for normalisation (VSN) and compare its efficiency with that of the commonly used standard normal variate (SNV) technique for the removal of unwanted illumination effects. Spectral images of potato plants were used for testing the correction. The results showed that the VSN outperformed SNV in removing illumination effects from the images of plants. The results show that the VSN approach to illumination correction can support high-throughput plant phenotyping where spectral imaging is used as a continuous monitoring tool.

中文翻译：

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利用变量排序进行归一化以校正马铃薯植物近距离光谱图像中的照明效果

可见光和近红外光谱成像是快速评估植物生物物理特性的关键无损技术。植物近距离光谱成像的一个主要挑战是光照效应引起的光谱变化，这可能会由于生理化学差异而掩盖信号。在目前的工作中，我们描述了一种称为归一化变量排序 (VSN) 的新散射校正技术，并将其效率与常用的标准正态变量 (SNV) 技术的效率进行比较，以消除不需要的照明效果。马铃薯植物的光谱图像用于测试校正。结果表明，VSN 在去除植物图像中的光照效应方面优于 SNV。