Development of spectral indices for detecting and identifying plant diseases

Development of spectral indices for detecting and identifying plant diseases Spectral vegetation indices (SVIs) have been shown to be useful for an indirect detection of plant diseases. However, these indices have not been evaluated to detect or to differentiate between plant diseases on crop plants. The aim of this study was to develop specific spectral disease indices (SDIs) for the detection of diseases in crops. Sugar beet plants and the three leaf diseases Cercospora leaf spot, sugar beet rust and powdery mildew were used as model system. Hyperspectral signatures of healthy and diseased sugar beet leaves were assessed with a non-imaging spectroradiometer at different developing stages and disease severities of pathogens. Significant and most relevant wavelengths and two band normalized differences from 450 to 950 nm, describing the impact of a disease on sugar beet leaves were extracted from the data-set using the RELIEF-F algorithm. To develop hyperspectral indices for the detection of sugar beet diseases the best weighted combination of a single wavelength and a normalized wavelength difference was exhaustively searched testing all possible combinations. The optimized disease indices were tested for their ability to detect and to classify healthy and diseased sugar beet leaves. With a high accuracy and sensitivity healthy sugar beet leaves and leaves, infected with Cercospora leaf spot, sugar beet rust and powdery mildew were classified (balanced classification accuracy: 89%, 92%, 87%, 85%, respectively). Spectral disease indices were also successfully applied on hyperspectral imaging data and on non-imaging data from a sugar beet field. Specific disease indices will improve disease detection, identification and monitoring in precision agriculture applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Development of spectral indices for detecting and identifying plant diseases

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Publisher
Elsevier
Copyright
Copyright © 2012 Elsevier Inc.
ISSN
0034-4257
D.O.I.
10.1016/j.rse.2012.09.019
Publisher site
See Article on Publisher Site

Abstract

Spectral vegetation indices (SVIs) have been shown to be useful for an indirect detection of plant diseases. However, these indices have not been evaluated to detect or to differentiate between plant diseases on crop plants. The aim of this study was to develop specific spectral disease indices (SDIs) for the detection of diseases in crops. Sugar beet plants and the three leaf diseases Cercospora leaf spot, sugar beet rust and powdery mildew were used as model system. Hyperspectral signatures of healthy and diseased sugar beet leaves were assessed with a non-imaging spectroradiometer at different developing stages and disease severities of pathogens. Significant and most relevant wavelengths and two band normalized differences from 450 to 950 nm, describing the impact of a disease on sugar beet leaves were extracted from the data-set using the RELIEF-F algorithm. To develop hyperspectral indices for the detection of sugar beet diseases the best weighted combination of a single wavelength and a normalized wavelength difference was exhaustively searched testing all possible combinations. The optimized disease indices were tested for their ability to detect and to classify healthy and diseased sugar beet leaves. With a high accuracy and sensitivity healthy sugar beet leaves and leaves, infected with Cercospora leaf spot, sugar beet rust and powdery mildew were classified (balanced classification accuracy: 89%, 92%, 87%, 85%, respectively). Spectral disease indices were also successfully applied on hyperspectral imaging data and on non-imaging data from a sugar beet field. Specific disease indices will improve disease detection, identification and monitoring in precision agriculture applications.

Journal

Remote Sensing of EnvironmentElsevier

Published: Jan 21, 2013

References

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