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Characterization and identification of leaf-scale wheat powdery mildew using a ground-based hyperspectral imaging system

文献类型： 外文期刊

作者： Zhao Jinling 1 ; Huang Wenjiang 1 ; Zhang Dongyan 1 ; Luo, J. 1 ; Zhang Jingcheng 1 ; Huang Linsheng 1 ; Chen, S. 3 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing 100101, Peoples R China

3.Xinjiang Vocat & Tech Coll, Urumqi 830000, Peoples R China

关键词： Powdery Mildew;Leaf-Scale Wheat;Hyperspectral Imaging System;Texture Analysis

期刊名称：DISASTER ADVANCES （ 影响因子：2.272； 五年影响因子：1.886 ）

ISSN： 0974-262X

年卷期： 2012 年 5 卷 4 期

页码：

收录情况： SCI

摘要： The objective of this study is to characterize and identify wheat leaves infected with powdery mildew using a domestic-made ground-based hyperspectral push-broom imaging spectrometer (PIS) with a spectral resolution of 2 mm and a spatial resolution of 5-10 nm. After performing a data preprocessing including image mosaicing, reflectance conversion and spectral smoothing, the image and spectral characteristics were investigated based on the high spatial and spectral resolution hyperspectral data cube acquired by this system. To explore the image characteristics, occurrence-based texture filters were utilized and their combination of data range, mean, variance were proved to be effective in differentiating disease spots from normal leaves. On the basis of identifying characteristic bands [(10 red bands (675.1-681.1 nm) and 10 near-infrared bands (706.2-712.1 nm) were respectively averaged)] sensitive to this disease, an image feature space (X axis: red; Y axis: NIR) was built to identify disease spots by a linear regression model (y=3.48*x-7.57) which was constructed using a total of 220 pixels from normal leaf and disease spot. To validate the identification accuracy of the model, 120 pixels were used and the overall classification accuracy reached 92.5%. The misclassification was caused due to nonuniform lighting in the process of scanning. Final identification results indicated that corresponding texture and spectral information were greatly enhanced due to the influence of pustular spots of powdery mildew. The analysis results demonstrated that it was feasible to identify disease spots of powdery mildew using the PIS.

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