Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases

Abstract

Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected. Light microscopy was used to describe the morphological changes in the host tissue due to pathogen colonisation. Under controlled conditions a hyperspectral imaging line scanning spectrometer (ImSpector V10E) with a spectral resolution of 2.8 nm from 400 to 1000 nm and a spatial resolution of 0.19 mm was used for continuous screening and monitoring of disease symptoms during pathogenesis. A pixel-wise mapping of spectral reflectance in the visible and near-infrared range enabled the detection and detailed description of diseased tissue on the leaf level. Leaf structure was linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, the pathogens caused disease-specific spectral signatures. The influence of the pathogens on leaf reflectance was a function of the developmental stage of the disease and of the subarea of the symptoms. Spectral reflectance in combination with Spectral Angle Mapper classification allowed for the differentiation of mature symptoms into zones displaying all ontogenetic stages from young to mature symptoms. Due to a pixel-wise extraction of pure spectral signatures a better understanding of changes in leaf reflectance caused by plant diseases was achieved using HSI. This technology considerably improves the sensitivity and specificity of hyperspectrometry in proximal sensing of plant diseases.

Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

“…Indeed, SAM achieved very good classification results in several studies. On sugar beet, Cercospora leaf spots could be correctly evaluated by up to 98%, while powdery mildew and rust were recognized with accuracies of up to 97% and 62%, respectively [85]. Considering a rating error of 10%, head blight infection was correctly classified up to 87% [45].…”

“…Thus, SAM may be optimal for classifications under semi-practical conditions [85,86]. Indeed, SAM achieved very good classification results in several studies.…”

Estimation of Plant Leaf Area using Java Image Processing Techniques

“…The software consists of User Friendly Graphical User Interface to make it simple in use. The methodology is as under [7].…”

Unraveling the molecules hidden in the gray shadows of quantitative disease resistance to pathogens

“…With these recent technologies, all of these parameters can be measured, even in the early plant phenological stages (Mutka and Bart, 2015). Wheat and sugarcane are some crops where these technologies have been used for detection and study of QDR (Mahlein et al, 2012;Bauriegel and Herppich, 2014;Mutka et al, 2016). Despite the fact that these techniques require a large number of previous evaluations in order to set the parameters for each disease, their potential in phenotyping plant disease is undeniable.…”