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

Abstract

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 Abstract: 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 490 780 nm (r 2 = 0.69), 510 780 nm (r 2 = 0.74), 516 1300 nm (r 2 = 0.62) and 540 1300 nm (r 2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490 780 nm 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.

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

“…It was found that most of the response bands were located in the visible region, and the two spectral ranges at 615-621 nm and 693-696 nm were the most effective to identify PM, YR and NW. This result was consistent with the finding of Graeff et al [41] . As shown in Table 2, six vegetation indexes were selected, including NPCI, ARI, MCARI, TCARI, PhRI and PRI.…”

Risk assessment posed by diseases in context of integrated management of wheat

“…From an economic point of view, the most severe diseases of wheat in the last 5 years in Poland and all of Europe have been: powdery mildew-Blumeria graminis (DC.) Speer [10,22,36,64], brown rust of leaves-Puccinia recondita Rob. Ex Desm f. sp.…”

Low-cost assessment of wheat resistance to yellow rust through conventional RGB images

“…As an alternative, the use of conventional digital images to derive green vegetation indices to predict yield and resistance to biotic stresses (caused by pests and diseases) has been reported in recent years (Diéguez-Uribeondo et al, 2003;Graeff et al, 2006;Mirik et al, 2006). Thus the low cost of red, green, blue (RGB) digital cameras makes them an attractive alternative for applications in precision agriculture and/or high-throughput phenotyping (Reyniers et al, 2004;Cabrera-Bosquet et al, 2012).…”

“…The b component has been claimed to be used for the calculation of the onset of senescence because it measures scalars of the color change that best describes the typical color shifts into yellow that occur during senescence in wheat (Kipp et al, 2014). In that sense, evaluation of plant biomass and the leaf area index in response to the water regime (Casadesús et al, 2007), or the impact of diseases such as brown-spot disease in rice and powdery mildew in wheat (Graeff et al, 2006;Kurniawati et al, 2009) are examples supporting the usefulness of these color traits. However, the applications of digital image analysis by different color bands to evaluate grain yield loss and changes in related physiological traits under rust-infection have not been assayed yet.…”