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Remote Sens. 2014, 6(6), 4723-4740; doi:10.3390/rs6064723

Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina)

1
Remote Sensing Group, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697-15433, Iran
2
Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Ultimo, NSW 2007, Australia
*
Authors to whom correspondence should be addressed.
Received: 24 February 2014 / Revised: 23 April 2014 / Accepted: 4 May 2014 / Published: 26 May 2014
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Abstract

Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat’s infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94. View Full-Text
Keywords: wheat leaf rust; hyperspectral measurement; spectral disease indices; disease symptoms wheat leaf rust; hyperspectral measurement; spectral disease indices; disease symptoms
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MDPI and ACS Style

Ashourloo, D.; Mobasheri, M.R.; Huete, A. Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina). Remote Sens. 2014, 6, 4723-4740.

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