Table_2_A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging.docx

Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

盐胁迫（Salinity stress）对作物生产力与产量具有显著负面影响。本研究的核心目标是利用高光谱成像（hyperspectral imaging）对小麦的耐盐性进行定量排序。实验采用水培系统（hydroponic system）培养4个小麦品系，设置对照组与盐处理组（分别添加0 mM与200 mM氯化钠（NaCl））。在施加盐胁迫后1天、尚未出现可见症状时采集高光谱图像。完成必要的预处理任务后，利用连续体积最大化法（successive volume maximization）在每张图像中识别出两个端元（endmembers），分别对应两种处理条件。为简化图像分析与解读流程，本研究提出一种逐向量相似度测量（vector-wise similarity measurement）方法，用于计算所有像素与盐胁迫端元的相似度。该方法可将高维高光谱图像降维为一维灰度图像，同时保留全部相关信息。随后采用两种方法分析灰度图像：配对分配最小差值法（minimum difference of pair assignments）与贝叶斯方法（Bayesian method）。两种方法得到的耐盐性排序结果一致，且与常规表型分析实验（conventional phenotyping experiments）得到的预期排序及已知耐盐性历史证据相符。本研究证实了机器学习（machine learning）在植物表型分析（phenotyping of plants）的高光谱图像分析中的应用价值，该方案具备定量、可解释且非侵入式的特点。