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 R 2 between the estimated and the observed was as highas 0.94.
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.
Leaf water content is an important parameter in environmental monitoring. The present study investigated the relation between leaf Equivalent Water Thickness (EWT) as a parameter to estimate the leaf water content and the reflectance in 400-2,500 nm spectral range. The data used were the well-known Leaf Optical Properties Experiment 93 (LOPEX93) field collected data. Four hundred leaf samples were used, 320 of which for modelling and the remaining 80 for testing the model. Four different approaches were investigated in this study: 1) linear regression between reflectance in individual wavelength and EWT; 2) the difference of reflectance in two wavelengths and EWT; 3) ratio of reflectance in two wavelengths and EWT; and finally 4) the normalized difference of reflectance in two different wavelengths and EWT. The results showed that the band combinations such as ratio and normalized difference had higher regressions with leaf water content. In addition, the findings of this study showed that some parts of the near infrared (NIR) and short wave infrared (SWIR) of the spectrum provided higher accuracies in EWT assessment, and correlations of more than 90% were achieved. Finally, this investigation showed that a wide range of wavelengths could be used for EWT assessment task. Despite the general belief in using water absorption bands for leaf water content assessment, this study shows that water absorption bands are not necessarily productive as other wavelengths have the potential to generate better results.
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