NDLI: Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

Content Provider	Springer Nature Link
Author	Mahlein, A. K. Steiner, U. Dehne, H. W. Oerke, E. C.
Copyright Year	2010
Abstract	This study examines the potential of hyperspectral sensor systems for the non-destructive detection and differentiation of plant diseases. In particular, a comparison of three fungal leaf diseases of sugar beet was conducted in order to facilitate a simplified and reproducible data analysis method for hyperspectral vegetation data. Reflectance spectra (400–1050 nm) of leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew and rust, respectively, were recorded repeatedly during pathogenesis with a spectro-radiometer and analyzed for disease-specific spectral signatures. Calculating the spectral difference and reflectance sensitivity for each wavelength emphasized regions of high interest in the visible and near infrared region of the spectral signatures. The best correlating spectral bands differed depending on the diseases. Spectral vegetation indices related to physiological parameters were calculated and correlated to the severity of diseases. The spectral vegetation indices Normalised Difference Vegetation Index (NDVI), Anthocyanin Reflectance Index (ARI) and modified Chlorophyll Absorption Integral (mCAI) differed in their ability to assess the different diseases at an early stage of disease development, or even before first symptoms became visible. Results suggested that a distinctive differentiation of the three sugar beet diseases using spectral vegetation indices is possible using two or more indices in combination.
Starting Page	413
Ending Page	431
Page Count	19
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	11
Issue Number	4
e-ISSN	15731618
Language	English
Publisher	Springer US
Publisher Date	2010-06-13
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cercospora beticola Erysiphe betae Uromyces betae Sugar beet Hyperspectral Remote sensing Disease detection Spectral signature Vegetation indices Meteorology/Climatology Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Remote Sensing/Photogrammetry Soil Science & Conservation Agriculture
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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