Using In-Situ Hyperspectral Data for Detecting and Discriminating Yellow Rust Disease from Nutrient Stresses

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yellow rust, water stress, nitrogen stress, hyperspectral, Physiological Reflectance Index (PhRI)

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Hyperspectral remote sensing is one of the advanced and effective techniques in disease monitoring and mapping. However, difficulty in discriminating disease from some frequent nutrient stresses largely hampers the practical use of this technique. This study conducted a series of normalization processes on canopy-scale, ground-based measurements of hyperspectral reflectance of a yellow rust disease inoculation treatment and a nutrient stressed treatment to detect and discriminate yellow rust disease from nutrient stresses. The normalization processes were implemented to minimize the effects of differences in illuminating conditions, measuring dates, cultivars and soil backgrounds on the target spectra. Per an independent t-test, the responses of a total of 38 commonly used spectral features for the yellow rust disease and different forms of nutrient stresses were examined at 5 major growth stages. It was found that the 36 spectral features were sensitive to water associated stresses; 28 spectral features were sensitive to yellow rust disease; and the 18 spectral features were sensitive to individual nitrogen stresses at least at one growth stage. Four vegetation indices were identified as those persistently having a response to yellow rust at 4 out of 5 growth stages. However, further independent t-test analysis showed that all the four vegetation indices also responded to several nutrient stresses, except for the Physiological Reflectance Index (PhRI), which was only sensitive to the yellow rust disease at all growth stages. The results clearly showed the potential of PhRI for detecting yellow rust disease under complicated farmland circumstances. The application of the index may lead to a more accurate and objective result for detecting and mapping the spatial distributions of yellow rust disease with hyperspectral imagery.

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Field Crops Research, v. 134, p. 165-174