Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages

Abstract

Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350–1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460–720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568–709 nm and 725–1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R2 of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R2 of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage.

In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features

“…This effect has been previously described for various patho-systems (e.g. Yu et al, 2018;Zhang et al, 2012;Zheng et al, 2019).…”

“…context specificity). This problem has previously been described in detail with regard to growth stages of the crop, different phases in the pathogenesis and the presence of other stresses (Devadas et al, 2015;Zhang et al, 2012;Zheng et al, 2019).…”

“…specific to the genotype, growth stage and/or site and environment under study; see e.g. Delalieux et al, 2007;Zhang et al, 2012;Zheng et al, 2019). Accordingly, identified spectral features and corresponding thresholds or calibration curves are not sufficiently robust (i.e.…”

In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

“…So far, various field measurements on the ground canopy scale of cereal crops have focused on the detection of biotrophic diseases such as yellow rust [3,10,13,[23][24][25], brown rust [18,26], and powdery mildew of wheat [19]. This might be due to the fact that biotrophic diseases are more likely to appear on the upper leaf layers because of wind distribution and a preference for fresh and healthy leaf tissue [27].…”

“…Less important was the NIR wavebands > 800 nm and the red part of the spectrum. Various works have shown that VIs using wavelengths out of these spectral regions can be successfully used to detect rust diseases of wheat [17,18,25], or even for necrotrophic diseases of other crop plants such as groundnuts [42]. In the literature, it has been described that pigments and water influence the absorbance and reflectance of light with plant interactions [43][44][45].…”