Utilizing Hyper-Spectral No-Image Measurement to Assess the Development of disease severity of Cercospora Leaf Spot disease in sugar beet canopy | ||||
| Journal of Plant Production Sciences | ||||
| Volume 11, Issue 1, June 2022, Page 37-45 PDF (439.7 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jpps.2022.234798 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Ahmed Ameen Abdullah; Mohamed O. Arnous ; Tarek Bayoumi; Heba M. Abd El. Nabi | ||||
| Abstract | ||||
| Sugar beet (Beta vulgaris) in Egypt is second important source sugar. In Egypt sugar beet is attacked by Cercospora leaf spot (CLS) caused by Cercospora beticola fungus. Remote sensing and GIS represent spectral signature and vegetation indices were used in the present study to determine the ability of these technique in the plant pathology sector.spectral signature and vegetation indices associated with infection of sugar beet crops by CLS disease using Hyperspectral No-image measurements SpectraPen SP100. Depend on the reaction between selecting wavelength values (R490, R560, R655, R705, R740 and R775) with canopy of sugar beet plants. Using spectral signature and vegetation indices (NDVI, SR, OSAVI, AR1 and AR2) result revealed that succeeded in determining the change in plant infected canopy causing by CLS disease of sugar beet. The result showed that spectral reflectance in selecting wavelength can discriminating between healthy and infected plant which represented (visible, red-edge and near-infrared) which have the best result to detect CLS and high correlation with disease severity of CLS. Result showed that. Result showed that vegetation indices NDVI have the highest correlation followed by AR1 and AR2. | ||||
| Keywords | ||||
| Sugar beet; Cercospora leaf spot; Spectral signature; Vegetation indices; Remote sensing | ||||
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