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.
Highlights
Yellow rust disease, caused by the fungus Puccinia striiformis, is a serious threat to wheat production and impacts the yield and quality of wheat [1,2]
According to previous studies related to the remotely-sensed detection of wheat infected by yellow rust disease, we found that anthocyanin reflectance index (ARI) and photochemical reflectance index (PRI) were reported as efficiently vegetation indices for yellow rust disease monitoring at the canopy scale [4,22], which was consistent with our results
The timely monitoring of wheat infected by yellow rust disease is critical for agricultural management of the growth stage
Summary
Yellow rust disease, caused by the fungus Puccinia striiformis, is a serious threat to wheat production and impacts the yield and quality of wheat [1,2]. In extreme situations of very susceptible cultivars and under favorable weather conditions, yellow rust can reduce the yield by 100% [4]. Conventional stress-detection methods usually range from detection by the naked eye to random monitoring, which is highly subjective, labor intensive, and time consuming. Even worse, when management and policy decisions are based on imprecise and inaccurate data from traditional monitoring results to evaluate the damage, it may cause costly mistakes [5]. The timely detection of crop diseases at different growth stages are critical to the effective management of the economy and agriculture [6]
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