Assessing Wheat Yellow Rust Disease through Hyperspectral Remote Sensing
Other literature type
Sahoo, R. N.
Gupta, V. K.
(issn: 2194-9034, eissn: 2194-9034)
The potential of hyperspectral reflectance data was explored to assess severity of yellow rust disease (Biotroph Pucciniastriiformis)
of winter wheat in the present study. The hyperspectral remote sensing data was collected for winter wheat (Triticum aestivum L.)
cropat different levels of disease infestation using field spectroradiometer over the spectral range of 350 to 2500 nm. The partial least
squares (PLS) and multiple linear (MLR) regression techniques were used to identify suitable bands and develop spectral models for
assessing severity of yellow rust disease in winter wheat crop. The PLS model based on the full spectral range and n = 36, yielded a
coefficient of determination (R2) of 0.96, a standard error of cross validation (SECV) of 12.74 and a root mean square error of cross
validation (RMSECV) of 12.41. The validation analysis of this PLS model yielded r2 as 0.93 with a SEP (Standard Error of
Prediction) of 7.80 and a RMSEP (Root Mean Square Error of prediction) of 7.46. The loading weights of latent variables from PLS
model were used to identify sensitive wavelengths. To assess their suitability multiple linear regression (MLR) model was applied on
these wavelengths which resulted in a MLR model with three identified wavelength bands (428 nm, 672 nm and 1399 nm). MLR
model yielded acceptable results in the form of r2 as 0.89 for calibration and 0.90 for validation with SEP of 3.90 and RMSEP of
3.70. The result showed that the developed model had a great potential for precise delineation and detection of yellow rust disease in
winter wheat crop.