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Research on Diagnosis Characteristics of Wheat Powdery Mildew Under Different Severity Grading Standards

文献类型： 外文期刊

作者： Zhang, Dongyan 1 ; Yin, Xun 1 ; Lin, Fenfang 2 ; Huang, Linsheng 1 ; Zhao, Jinling 1 ; Liu, Yu 1 ; Ma, Wei 3 ; Hong, Qi 1 ;

作者机构： 1.Anhui Univ, Anhui Engn Lab Agroecol Big Data, Hefei, Peoples R China

2.Nanjing Univ Informat Sci & Technol, Sch Geog & Remote Sensing, Nanjing, Peoples R China

3.Beijing Res Ctr Intelligent Equipment Agr, Beijing, Peoples R China

关键词： Sensitive hand; Vegetation index; SVM; Wheat powdery mildew

期刊名称：2019 8TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS)

ISSN： 2334-3168

年卷期： 2019 年

页码：

收录情况： SCI

摘要： Wheat powdery mildew (Blumeria graminis Dc.speer) is one of the most devastating crop diseases in the globe. Thinking of economic effective and environmental protection value, early detection of the severity of wheat powdery mildew can provide important information and technical support for disease prevention. In this study, the wheat leaves infected powdery mildew were chosen as observation objects, the obtained hyperspectral imagery data was pre-processed by reflectance calculation and noise elimination. After the disease-infected samples with different severities were divided into three-levels, four-levels, and five-levels, the effects of samples classification on identification of the disease were explored. Subsequently, the Relief-F algorithm was used to screen the sensitive bands of the disease in the early and mid-late growth stages, to observe the wavelengths change of disease identification in different developmental periods. The results showed that the sensitive bands of disease detection respectively locate at 700 nm and 680 nm for the early and mid-late growth stages, and the position of sensitive wavelength moves toward the short-wave direction as the disease worsens. On the basis, Calculating the powdery mildew disease index (PMDI) and nine kinds of common vegetation indexes, to compare their effects on disease identification, the study found that when the samples were divided into four levels, the determination coefficient R-2 of PMDI is the highest. For the early and mid-late infection stages, the R-2 are respectively 0.763 and 0.766. Furthermore, the corresponding SVM models were established in the different developmental periods, the classification accuracy is 90.63% at the early growth stage, while that one is the 84.62% at mid-late developmental period. The above results show that PMDI calculated by the sensitive band screening has good effective on identifying the severity of the disease, especially there is a good potential at the early growth stage.

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