Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements

• Main Authors: Davoud Ashourloo, Mohammad Reza Mobasheri, Alfredo Huete
• Format: Article
• Language: English
• Published: MDPI AG 2014-06-01
• Series: Remote Sensing
• Subjects: hyperspectral data; vegetation index; wheat rust disease
• Online Access: http://www.mdpi.com/2072-4292/6/6/5107

Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease.