NDLI: New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases

Content Provider	IEEE Xplore Digital Library
Author	Wenjiang Huang Qingsong Guan Juhua Luo Jingcheng Zhang Jinling Zhao Dong Liang Linsheng Huang Dongyan Zhang
Copyright Year	2008
Abstract	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 ( \mbiR^{2 =} 0.86) between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application.
Starting Page	2516
Ending Page	2524
Page Count	9
File Size	1582514
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Agriculture Indexes Monitoring Hyperspectral imaging yellow rust Aphids canopy reflectance hyperspectrum new spectral indices (NSIs) powdery mildew winter wheat
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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