Li Xiao-long, Qin Feng, Zhao Long-lian, Li Jun-hui, Ma Zhan-hong, Wang Hai-guang
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Feb;35(2):367-71.
Wheat stripe rust caused by Puccinia striiformis f. sp. tritici, is an economically important disease in the world. It is of great significance to assess disease severity of wheat stripe rust quickly and accurately for monitoring and controlling the disease. In this study, wheat leaves infected with stripe rust pathogen under different severity levels were acquired through artificial inoculation in artificial climate chamber. Thirty wheat leaves with disease severity equal to 1%, 5%, 10%, 20%, 40%, 60%, 80% or 100% were picked out, respectively, and 30 healthy leaves were chosen as controls. A total of 270 wheat leaves were obtained and then their near infrared spectra were measured using MPA spectrometer. According to disease severity levels, 270 near infrared spectra were divided into 9 categories and each category included 30 spectra. From each category, 7 or 8 spectra were randomly chosen to make up the testing set that included 67 spectra. The remaining spectra were treated as the training set. A qualitative model for identification and classification of disease severity of wheat stripe rust was built using near infrared reflectance spectroscopy (NIRS) technology combined with discriminant partial least squares (DPLS). The effects of different preprocessing methods of obtained spectra, ratios between training sets and testing sets, and spectral ranges on qualitative recognition results of the model were investigated. The optimal model based on DPLS was built using cross verification method in the spectral region of 4000-9000 cm(-1) when "centralization" was used as the preprocessing method of spectra and the spectra were divided into the training set and the testing set with the ratio equal to 3:1. Accuracy rate of the training set was 95.57% and accuracy rate of the testing set was 97.01%. The results showed that good recognition performance could be acquired using the model based on DPLS. The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for identification and classification of disease severity of wheat stripe rust. A new method was provided for monitoring and assessment of wheat stripe rust.
由条形柄锈菌小麦专化型（Puccinia striiformis f. sp. tritici）引起的小麦条锈病是世界上一种具有重要经济影响的病害。快速、准确地评估小麦条锈病的病情严重程度对于该病害的监测和防控具有重要意义。在本研究中，通过在人工气候箱中进行人工接种，获取了不同严重程度下感染条锈病菌的小麦叶片。分别挑选出病情严重程度等于1%、5%、10%、20%、40%、60%、80%或100%的30片小麦叶片，并选取30片健康叶片作为对照。共获得270片小麦叶片，然后使用MPA光谱仪测量它们的近红外光谱。根据病情严重程度水平，将270个近红外光谱分为9类，每类包含30个光谱。从每类中随机选择7个或8个光谱组成包含67个光谱的测试集。其余光谱作为训练集。利用近红外反射光谱（NIRS）技术结合判别偏最小二乘法（DPLS）建立了小麦条锈病病情严重程度的识别与分类定性模型。研究了所得光谱的不同预处理方法、训练集与测试集的比例以及光谱范围对模型定性识别结果的影响。当采用“中心化”作为光谱预处理方法且光谱按3:1的比例分为训练集和测试集时，在4000 - 9000 cm(-1)光谱区域使用交叉验证法建立了基于DPLS的最优模型。训练集的准确率为95.57%，测试集的准确率为97.01%。结果表明，基于DPLS的模型具有良好的识别性能。结果表明，本研究提出的利用近红外反射光谱技术的方法对于小麦条锈病病情严重程度的识别与分类是可行的。为小麦条锈病的监测与评估提供了一种新方法。
