Identifying plant pathogens for disease diagnosis and disease control strategy making is of great significance. In this study, based on near-infrared spectroscopy, a method for identifying three kinds of pathogens causing wheat smuts, including Tilletia foetida, Ustilago tritici, and Urocystis tritici, was investigated. Based on the acquired near-infrared spectral data of the teliospore samples of the three pathogens, pathogen identification models were built in different spectral regions using distinguished partial least squares (DPLS), backpropagation neural network (BPNN), and support vector machine (SVM). Satisfactory identification results were achieved using the DPLS, BPNN, and SVM models built in each of the 22 spectral regions. By contrast, the modeling effects of DPLS and SVM were better than those of BPNN. The modeling ratio of the training set to the testing set affected the identification results of the BPNN models more than those obtained using the DPLS and SVM models. In this study, a rapid, accurate, and nondestructive method was provided for plant pathogen identification, and some basis was provided for disease diagnosis, pathogen monitoring, and disease control. Moreover, some methodological references and supports were provided for identification of quarantine wheat smut fungi in plant quarantine.

中文翻译：

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基于近红外光谱技术的虎杖，小麦草和小麦草的鉴定

识别植物病原体对疾病的诊断和疾病控制策略的制定具有重要意义。在这项研究中，基于近红外光谱法，一种识别导致小麦黑穗病的三种病原体的方法，包括Tilletia foetida，Ustilago tritici和Urocystis tritici，进行了调查。基于获取的三种病原体的孢子样本的近红外光谱数据，使用可分辨的偏最小二乘（DPLS），反向传播神经网络（BPNN）和支持向量机（SVM）在不同光谱区域建立病原体识别模型。使用建立在22个光谱区域中的每个光谱区域中的DPLS，BPNN和SVM模型，可以获得令人满意的识别结果。相比之下，DPLS和SVM的建模效果要好于BPNN。与使用DPLS和SVM模型获得的结果相比，训练集与测试集的建模比率对BPNN模型的识别结果的影响更大。在这项研究中，提供了一种快速，准确且无损的方法来鉴定植物病原体，并为疾病诊断提供了一些依据，病原体监测和疾病控制。此外，提供了一些方法学参考和支持，以鉴定植物检疫中的隔离小麦黑穗病真菌。