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Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements

文献类型： 外文期刊

作者： Zhang, Jing-Cheng 1 ; Pu, Rui-liang 2 ; Wang, Ji-hua 1 ; Huang, Wen-jiang 1 ; Yuan, Lin 1 ; Luo, Ju-hua 1 ;

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

2.Univ S Florida, Dept Geog Environm & Planning, Tampa, FL USA

3.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst Applicat, Hangzhou 310029, Zhejiang, Peoples R China

关键词： Powdery mildew;Spectral feature;Partial least square regression (PLSR);Fisher linear discriminate analysis (FLDA);Cross validation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2012 年 85 卷

页码：

收录情况： SCI

摘要： Powdery mildew (Blumeria graminis) is one of the most destructive diseases, which has a significant impact on the production of winter wheat. Detecting powdery mildew via spectral measurement and analysis is a possible alternative to traditional methods in obtaining the spatial distribution information of the disease. In this study, hyperspectral reflectances of normal and powdery mildew infected leaves were measured with a spectroradiometer in a laboratory. A total of 32 spectral features (SFs) were extracted from the lab spectra and examined through a correlation analysis and an independent t-test associated with the disease severity. Two regression models: multivariate linear regression (MLR) and partial least square regression (PLSR) were developed for estimating the disease severity of powdery mildew. In addition, the fisher linear discriminant analysis (FLDA) was also adopted for discriminating the three healthy levels (normal, slightly-damaged and heavily-damaged) of powdery mildew with the extracted SFs. The experimental results indicated that (1) most SFs showed a clear response to powdery mildew; (2) for estimating the disease severity with SFs, the PLSR model outperformed the MLR model, with a relative root mean square error (RMSE) of 0.23 and a coefficient of determination (R-2) of 0.80 when using seven components; (3) for discrimination analysis, a higher accuracy was produced for the heavily-damaged leaves by FLDA with both producer's and user's accuracies over 90%; (4) the selected broad-band SFs revealed a great potential in estimating the disease severity and discriminating severity levels. The results imply that multispectral remote sensing is a cost effective method in the detection and mapping of powdery mildew. (C) 2012 Elsevier B.V. All rights reserved.

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