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Evaluation of Spectral Disease Index PMI to Detect Early Wheat Powdery Mildew using Hyperspectral Imagery Data

文献类型： 外文期刊

作者： Lin, Fenfang 1 ; Wang, Dandan 1 ; Zhang, Dongyan 2 ; Yang, Xiaodong 3 ; Yin, Xun 2 ; Wang, Daoyong 2 ;

作者机构： 1.Nanjing Univ Informat Sci & Technol, Sch Geog & Remote Sensing, Nanjing 210044, Jiangsu, Peoples R China

2.Anhui Univ, Anhui Engn Lab Agroecol Big Data, Hefei 230601, Anhui, Peoples R China

3.Minist Agr, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

关键词： Wheat powdery mildew; Hyperspectral imaging; Spectral disease index; Early detection

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURE AND BIOLOGY （ 影响因子：0.822； 五年影响因子：0.906 ）

ISSN： 1560-8530

年卷期： 2018 年 20 卷 9 期

页码：

收录情况： SCI

摘要： Powdery mildew is one of the most widely destructive plant diseases, particularly infecting winter wheat. Early detection of wheat powdery mildew is of importance, which is useful to reduce economic costs and environmental pollution. However, difficulties emerge at early development stages, due to slight variations of the characteristic symptoms. Fortunately, hyperspectral reflectance imaging has been proven as a powerful tool to detecting early disease severity in plant. In this study, hyperspectral imagery data of leaves were acquired at early stages of the disease in winter wheat. It was demonstrated that early powdery mildew could induce observable spectral changes in both visible and near infrared regions. Given that, powdery mildew indices (PMI) were constructed and showed the capability of distinguishing between normal and diseased leaves, although it displayed poor effects for differentiating disease-damaged levels of early powdery mildew and estimating disease severity. However, further study was carried out by combination of hyperspectral vegetation indices closely related to plant diseases. It was noticeable that the model of three indices of PRI, PSRI and ARI significantly increased the classification accuracy of various early disease levels, and the regression model of PMI, PSRI and ARI apparently improved the estimation accuracy of disease severity. These valuable results could be used to prevent the development and the spread of the disease, and particularly beneficial to develop a portable or automated sensor in precision agriculture. (C) 2018 Friends Science Publishers

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