Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

Weeds compete with crops and are hard to differentiate identify due their similarities in color, shape, size. In this study, the weed species present sorghum (sorghum bicolor (L.) Moench) fields, such as amaranth (Amaranthus macrocarpus), pigweed (Portulaca oleracea), mallow (Malva sp.), nutgrass (Cyperus rotundus), liver seed grass (Urochoa panicoides), Bellive (Ipomea plebeian), were discriminated using hyperspectral data detected analyzed multispectral images. Discriminant analysis (DA) was used most significant spectral bands order discriminate weeds from data. The results demonstrated good separation accuracy for Amaranthus macrocarpus, Urochoa panicoides, Malva sp., Cyperus rotundus, Sorghum Moench at 440, 560, 680, 710, 720, 850 nm. Later, images of these six collected detect crop fields object-based image (OBIA). showed that differences between detectable selected bands, an unmanned aerial vehicle. Here, highest spatial resolution had detection. It concluded each successfully higher resolution.