High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.

中文翻译：

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高光谱玉米叶片图像的应力分布分析，以提高表型质量。

高通量成像技术已迅速发展用于农业植物表型分析。在当前大多数作物植物图像处理算法中，从图像中分割出植物冠层像素，并计算整个冠层的平均光谱，以预测植物的生理特征。但是，整个冠层的养分和压力水平差异很大。例如，通常在同一叶子上不同位置的土壤植物分析发展（SPAD）叶绿素仪读数之间存在数倍的差异。当前的植物图像处理算法无法提供令人满意的植物测量质量，因为平均颜色无法表征不同叶片的特征。与此同时，营养成分和压力分布模式具有独特的功能，可能为表型提供有价值的信号。开发更高水平的图像处理算法具有很大的潜力，该算法可以分析整个叶片上的养分和应力分布，从而提高表型测量的质量。为了分析玉米叶上的分布规律，本文提出了一种基于随机森林和叶面积缩放的新叶图像处理算法。以归一化植被指数（NDVI）为例，说明了新算法在区分不同氮胁迫水平方面的改进。通过将随机森林方法集成到算法中，成功地模拟了沿玉米叶片中肋骨方向的分布模式，并用于提高表型质量。该算法在不同基因型和氮素处理的玉米田表型分析中进行了测试。与传统的图像处理算法（例如，对整个叶片平均NDVI进行平均）相比，新算法可以更清楚地区分叶片与不同的氮处理和基因型。我们希望，除了NDVI之外，新的分布分析算法还可以类似的方式提高其他植物特征测量的质量。与传统的图像处理算法（例如，对整个叶片平均NDVI进行平均）相比，新算法可以更清楚地区分叶片与不同的氮处理和基因型。我们希望，除了NDVI之外，新的分布分析算法还可以类似的方式提高其他植物特征测量的质量。与传统的图像处理算法（例如，对整个叶片平均NDVI进行平均）相比，新算法可以更清楚地区分叶片与不同的氮处理和基因型。我们希望，除了NDVI之外，新的分布分析算法还可以类似的方式提高其他植物特征测量的质量。