Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements

By S. Graeff, Johanna Link, Wilhelm Claupein. Central European Journal of Biology, 2006

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The ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380–1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490780 nm (r2 = 0.69), 510780nm (r2 = 0.74), 5161300nm (r2 = 0.62) and 5401300 nm (r2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490780nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.

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Citations in the corpus

• Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “Plants may be affected by multiple pathogens at the same time during the growth process, and some different pathogens may produce similar symptoms and signs [102], [103] and the symptoms are not obvious at the early stage of plant diseases, which makes it easy to use naked eyes or simple computer vi”
• Advanced methods of plant disease detection. A review (2014, background): “…such as, among the most studied using RS: Fiji leaf gall sugarcane disease (Purcell et al. 2009), wheat powdery mildew and take-all disease (Graeff et al. 2006), curl mite (Stilwell et al. 2013), sugarcane yellow leaf virus (Grisham et al. 2010), grapevine viruses (Naidu et al. 2009), and…”
• Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects (2014, methodology): “For YR, PM and AH, the DI was determined by a visual estimation of the percent coverage of pustules/aphids on the leaf (Graeff et al., 2006; Zhang et al., 2012a; Luo et al., 2012).”