Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
Sensors (Basel). 2020 Jun 30;20(13):3659. doi: 10.3390/s20133659.
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.
高通量成像技术在农业植物表型分析中得到了迅速发展。目前大多数作物图像的处理算法，是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。然而，冠层内的养分和胁迫水平存在显著差异。例如，同一叶片不同位置的 SPAD 叶绿素计读取的叶绿素含量之间，其数值可能相差数倍。目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的叶片部位。同时，养分和胁迫分布模式具有独特的特征，可能为表型分析提供有价值的信号。开发一种更精细的图像处理算法，分析叶片内的养分和胁迫分布，以提高表型测量的质量，具有很大的潜力。本文提出了一种新的基于随机森林和叶片区域重缩放的叶片图像处理算法，用于分析玉米叶片上的分布模式。以归一化差异植被指数（NDVI）为例，证明了新算法在区分不同氮胁迫水平方面的改进。该算法将随机森林方法集成到算法中，成功地对玉米叶片中肋方向的分布模式进行了建模和利用，从而提高了表型分析的质量。该算法在不同基因型和氮处理的田间玉米植物表型分析中进行了测试。与传统的图像处理算法（例如，将 NDVI 平均值应用于整个叶片）相比，新算法能够更清晰地区分不同氮处理和基因型的叶片。我们期望，除了 NDVI 之外，新的分布分析算法还可以以类似的方式提高其他植物特征测量的质量。
