基于近红外光谱技术的小麦条锈病和叶锈病的早期诊断

为实现小麦条锈病和叶锈病的早期诊断，利用近红外光谱技术结合定性偏最小二乘法(DPLS)建立了一种鉴别这两种病害的方法。试验将150片小麦叶片(健康叶片、条锈病潜育叶片、条锈病发病叶片、叶锈病潜育叶片、叶锈病发病叶片各30片)分为5类，扫描获得近红外光谱，建立小麦叶片DPLS近红外光谱鉴别模型。原始光谱数据经二阶导数处理后，在4 000～8 000 cm-1范围内，当利用不同建模比建模时，建模集的平均识别率为96.56％，检验集的平均识别率为91.85％，证明了模型的稳定性。当建模比为2∶1、主成分数为10时，模型识别效果较好，建模集的识别准确率为97.00%，检验集的识别准确率为96.00%。表明应用近红外光谱技术建立的小麦条锈病和叶锈病早期诊断的定性鉴别方法是可行的。

Early Diagnosis of Wheat Stripe Rust and Wheat Leaf Rust Using Near Infrared Spectroscopy

In the present study, near-infrared reflectance spectroscopy (NIRS) technology was applied to implement early diagnosis of two kinds of wheat rusts, i.e. wheat stripe rust and wheat leaf rust, by detecting wheat leaves as disease symptom has not appeared. The wheat leaves were divided into five categories including healthy leaves, leaves in the incubation period infected with P. striiformis f. sp. tritici, leaves showing symptom infected with P. striiformis f. sp. tritici, leaves in the incubation period infected with P. recondita f. sp. tritici and leaves showing symptom infected with P. recondita f. sp. tritici. Near infrared spectra of 150 wheat leaves were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using distinguished partial least squares (DPLS). For building the model, second-order derivative method was regarded as the best preprocessing method of the spectra and the spectral region 4 000~8 000 cm-1 was regarded as the optimal spectral region. Using the model with different training sets and testing sets, the average identification rate of the training sets was 96.56% and the average identification rate of the testing sets was 91.85%. The results proved the model’s stability. The optimal identification rates were obtained while the ratio of training set to testing set was 2∶1 and the number of principal components was 10. The identification rate of the training set was 97.00% and the identification rate of the testing set was 96.00%. The results indicated that the identification method based on the NIRS technology developed in this study is feasible for early diagnosis of wheat stripe rust and wheat leaf rust.