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New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases

文献类型： 外文期刊

作者： Huang, Wenjiang 1 ; Guan, Qingsong 1 ; Luo, Juhua 3 ; Zhang, Jingcheng 4 ; Zhao, Jinling 2 ; Liang, Dong 2 ; Huang, Li 1 ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China

2.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

3.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing 210008, Peoples R China

4.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Aphids;canopy reflectance;hyperspectrum;new spectral indices (NSIs);powdery mildew;winter wheat;yellow rust

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2014 年 7 卷 6 期

页码：

收录情况： SCI

摘要： The vegetation indices from hyperspectral data have been shown to be effective for indirect monitoring of plant diseases. However, a limitation of these indices is that they cannot distinguish different diseases on crops. We aimed to develop new spectral indices (NSIs) that would be useful for identifying different diseases on crops. Three different pests (powdery mildew, yellow rust, and aphids) in winter wheat were used in this study. The new optimized spectral indices were derived from a weighted combination of a single band and a normalized wavelength difference of two bands. The most and least relevant wavelengths for different diseases were first extracted from leaf spectral data using the RELIEF-F algorithm. Reflectance of a single band extracted from the most relevant wavelengths and the normalized wavelength difference from all possible combinations of the most and least relevant wavelengths were used to form the optimized spectral indices. The classification accuracies of these new indices for healthy leaves and leaves infected with powdery mildew, yellow rust, and aphids were 86.5%, 85.2%, 91.6%, and 93.5%, respectively. We also applied these NSIs for nonimaging canopy data of winter wheat, and the classification results of different diseases were promising. For the leaf scale, the powdery mildew-index (PMI) correlated well with the disease index (DI), supporting the use of the PMI to invert the severity of powdery mildew. For the canopy scale, the detection of the severity of yellow rust using the yellow rust-index (YRI) showed a high coefficient of determination (R(2=)0.86) between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application.

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[2]CHARACTERIZATION OF POWDERY MILDEW IN WINTER WHEAT USING MULTI-ANGULAR HYPERSPECTRAL MEASUREMENTS. Jinling Zhao,Lin Yuan,Linsheng Huang,Dongyan Zhang,Jingcheng Zhang,Xiaohe Gu. 2013

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[15]Vegetation Cover Estimation Based on in-suit Hyperspectral Data: A Case Study for Meadow Steppe Vegetation in Inner Mongolia, China. Li, Xiao-Bing,Wang, Hong,Long, Hui-Ling. 2013

[16]Estimation of nitrogen status in middle and bottom layers of winter wheat canopy by using ground-measured canopy reflectance. Wang, ZJ,Wang, JH,Liu, LY,Huang, WJ,Zhao, CJ,Lu, YL. 2005

[17]Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat. Wang Jing,Jing Yuan-shu,Zhao Juan,Wang Jing,Huang Wen-jiang,Zhang Qing,Wang Li,Zhang Jing-cheng. 2015

[18]CONTINUOUS WAVELET ANALYSIS BASED SPECTRAL FEATURE SELECTION FOR WINTER WHEAT YELLOW RUST DETECTION. Zhang Jingcheng,Wang Jihua,Zhang Jingcheng,Luo Juhua,Huang Wenjiang,Wang Jihua,Luo Juhua. 2011

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[20]Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Zhang, Jingcheng,Huang, Wenjiang,Yuan, Lin,Luo, Juhua,Wang, Jihua,Zhang, Jingcheng,Pu, Ruiliang,Zhang, Jingcheng,Yuan, Lin,Wang, Jihua,Huang, Wenjiang. 2012

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