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

Abstract

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.

Assessing Changes in Potato Canopy Caused by Late Blight in Organic Production Systems Through Uav-Based Pushbroom Imaging Spectrometer

“…In fact, other authors identified spectral bands in the red and green regions as candidates to improved discrimination between healthy and diseased plants, at leaf level (Ashourloo et al, 2014;Mahlein et al, 2013). However, the dataset analysed here concerns only one acquisition date and spectral data at the plot level corresponded to all spectral signatures acquired, including areas…”

Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging

“…Studies have shown that once several sensitive bands are confirmed, the wheat powdery mildew could be diagnosed precisely by calculating a vegetation index (Mahlein et al, 2012). Table 2 shows several disease vegetation indexes (Devadas et al, 2009;Naidu et al, 2009;Ashourloo et al, 2014c). In order to enhance the disease features recognition; this study also used the red edge parameters, including the slope of the red edge, red edge position and the red edge area (Table 2) (Zhang et al, 2012).…”

“…However, the generalization and robustness of the models have to be verified in the scale of field with the data across crop varieties (Devadas et al, 2009;Ashourloo et al, 2014a;Huang et al, 2014). Up to now, there are still few literatures focused on crop disease recognition and assessment in the scale of crop canopy and field (Ashourloo et al, 2014b). Devadas et al (2009) found that healthy, diseased and shadowed leaves, wheat ear, wheat stems and soil constitutes a complex environmental background for wheat disease diagnosis in the field (Devadas et al, 2009).…”

“…The two spectrum ranges are the major sources of information for developing VIs (Vegetation Indices). The VIs developed in such a way have been widely used well in the scale of the crop leaf, canopy and field (Huang et al, 2007;Haboudane et al, 2008;Ashourloo et al, 2014b). Difference in spectral response of disease severities is the basis of optical diagnostics for crop diseases.…”

“…The results showed that two vegetation indices, ARI (Anthocynanin Reflectance Index) and TCARI (Transformed Chlorophyll Absorption Ratio Index), could recognize wheat stripe rust accurately in the scale of leaf. Ashourloo et al (2014c) used hyperspectral data to assess the different symptoms of wheat rust for wheat disease recognition using VIs (Ashourloo et al, 2014c;Huang et al, 2015). They selected 455 nm, 605 nm and 695 nm as the disease sensitive bands and built a new VI of wheat rust detection.…”