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.
Precision Agriculture – Springer Journals
Published: Jun 13, 2010
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