Mona Sayed Yones1*, Mohamed Amin Aboelghar1, Ghada Ali Khdery1, Abdelraouf Massoud Ali1,
Nasser Hussien Salem1, Eslam Farag1, Shireen Ahmed Mahmoud Ma’mon2
1National Authority of Remote Sensing and Space Science (NARSS), Cairo, Egypt
2Entomology Department, Faculty of Science, Ain Shams University, Cairo, Egypt
Identification of the best spectral zone and wavelength to be used for the discrimination of healthy and infected sugar beet plants and also to discriminate between the different infections of sugar beet plants is the goal achieved in this research. Field hyperspectral radiometer was used to measure spectral reflectance characteristics. By comparing spectral reflectance for the three infections of sugar beet plants (Cotton leaf worm, Aphid and Whiteflies), showed high pattern similarity. HSD Tukey’s analysis showed that the NIR and Blue spectral zone are the best for the discrimination between healthy sugar beet plant and the different infections; on the other hand SWIR-1 and SWIR-2 was the worst but Red and Green spectral zones showed reasonable discrimination. Also, Spectral discrimination was clearer in case of old leaves than young ones. Hence a result of this study is significant, as remote sensing technologies can be used for early detection for plants infections, and thus can be used for integrated pest management system.
Keywords: Hyperspectral data, Sugar beet, White fly, Aphid, Cotton leaf worm