Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 1413-1416, 2014
https://doi.org/10.5194/isprsarchives-XL-8-1413-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 1413-1416, 2014
https://doi.org/10.5194/isprsarchives-XL-8-1413-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Dec 2014

23 Dec 2014

Assessing Wheat Yellow Rust Disease through Hyperspectral Remote Sensing

G. Krishna1, R. N. Sahoo1, S. Pargal2, V. K. Gupta1, P. Sinha3, S. Bhagat4, M.S. Saharan5, R. Singh1, and C. Chattopadhyay4 G. Krishna et al.
  • 1Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, India
  • 2Division of Plant Physiology, Indian Agricultural Research Institute, New Delhi, India
  • 3Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, India
  • 4National Centre for Integrated Pest Management, New Delhi, India
  • 5Directorate of Wheat Research, Karnal, Haryana, India

Keywords: Wheat Yellow Rust, Hyperspectral Reflectance, Spectral Disease Indices, Disease Severity Score, PLSR, ANOVA, MLR

Abstract. 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.