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

The reflectance of leaves is the result of multiple interactions between incoming irradiation and biophysical (e.g. leaf surface, tissue structure) and biochemical characteristics (e.g. content of pigments and water) of plants [1-3]. Several studies have described the prospects of sensing leaf reflectance in the visible (VIS, 400-700 nm), near infrared (NIR, 700-1000 nm) and short wave infrared (SWIR, 1000-2500 nm) for detecting changes in plant vitality with emphasis on fungal plant diseases using non-imaging spectroradiometers [4-7]. Disease symptoms result from physiological changes in plant metabolism due to activities of pathogens [8]. The impact on physiology and phenology of plants varies with the type of host-pathogen interaction and may cause modifications in pigments, water content, and tissue functionality of plants or in the appearance of pathogen-specific fungal structures [9,10]. All these factors may change the spectral characteristics of plants. Knowledge on the effects of pathogens on the metabolism and structure of plant tissue is therefore essential for hyperspectral discrimination of healthy and diseased leaf and canopy elements [11].Hyperspectral imaging is an innovative technology with high potential for non-invasive sensing of the physiological status of vegetation [12-14] and may allow an objective and automatic assessment of the severity of plant diseases in combination with continuative data analysis methods [15]. Further to spectral information from non-imaging spectroradiometers, hyperspectral cameras enable the detection of spectral and spatial information of objects of interest. Hyperspectral imaging is expected to improve disease detection through a better examination of host-pathogen interactions [15,16]. Imaging sensor systems allow a pixel-wise attribution of disease-specific symptoms and healthy tissue and improve both, the specificity and sensitivity of disease detection by technical sensors [13].In most studies using hyperspectra