摘要
为实现对受到小麦条锈病菌侵染而尚未表现明显症状的小麦叶片进行早期检测，利用近红外光谱技术结合定性偏最小二乘法建立了小麦条锈病潜育期叶片定性识别模型。获取健康叶片30片、条锈病潜育期叶片330片（每天取30片，共11天）和发病叶片30片，扫描获得其近红外光谱曲线。采用内部交叉验证法建模，研究了不同谱区、建模比（建模集∶检验集）、光谱预处理方法和主成分数对建模识别效果的影响。在5400～6600和7600～8900 cm-1组合谱区内，建模比为4∶1、预处理方法为“散射校正”和主成分数为14时，所建模型识别效果较理想，建模集的识别准确率、错误率和混淆率分别为95.51％，1.28％和3.21％；检验集的识别准确率、错误率和混淆率分别为100.00％，0.00％和0.00％。结果表明，利用近红外光谱技术可在接种1天后（即提前11天）识别出健康小麦叶片和受到条锈病菌侵染的小麦叶片，并且可以识别不同潜育期天数的叶片。因此，利用近红外光谱技术对条锈病菌潜伏侵染检测是可行的，为该病早期诊断提供了一种新途径。
To realize the early detection of P. striiformis f. sp. tritici latent infections in wheat leaves while no disease symptoms appear ,a qualitative model for identification of the wheat leaves in the incubation period of stripe rust was built using near infrared reflectance spectroscopy （NIRS） technology combined with qualitative partial least squares （DPLS）. In this study ,30 leaf samples infected with P. striiformis f. sp. tritici were collected each day during the eleven-day incubation period. And 30 healthy leaf samples and 30 leaf samples showing disease symptoms infected with P. striiformis f. sp. tritici ,were also collect-ed as controls. In total ,there were 390 leaf samples that were divided into thirteen categories. Near infrared spectra of 390 leaf samples were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using cross verification method. The effects of different spectral ranges ,samples for building the model ,preprocessing methods of spectra and number of principal components on NIRS prediction results for qualitative identification were investigated. The optimal identification results were obtained for the model built in the combined spectral region of 5 400~6 600 and 7 600~8 900 cm -1 when the spectra were divided into the training set and the testing set with the ratio equal to 4∶1 ,“scatter correction”was used as the preprocessing method and the number of principal components was 14. Accuracy rate ,misjudgment rate and confusion rate of the training set were 95.51% ,1.28% and 3.21% ,respectively. And accuracy rate ,misjudgment rate and confusion rate of the testing set were 100.00% ,0.00% and 0.00% ,respectively. The result showed that using near infrared reflectance spectroscopy technology ,P. striiformis f. sp. tritici latent infections in wheat leaves could be detected as early as one day after inoculation （or 11 days before symptoms appearing ） and the number of days when the leaf has been infected could also be identified.The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for the identification of wheat leaves latently infected by P. striiformis f. sp. tritici. A new method based on NIRS was provided for the early detection of wheat stripe rust in this study.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2014年第7期1853-1858,共6页
Spectroscopy and Spectral Analysis
基金
国家科技支撑计划项目(2012BAD19B04)资助
关键词
近红外光谱
小麦条锈病
潜伏侵染
潜育期
定性识别
Near infrared spectroscopy
Wheat stripe rust
Latent infection
Incubation period
Qualitative identification
