###### Abstract  
Weeds compete with crops and are hard to differentiate and identify due to their similarities in color, shape, and size. In this study, the weed species present in sorghum (_sorghum bicolor_ ([PERSON]) fields, such as amaranth (_Amarantulus macrocarpus_), pigweed (_Portulaca oleraca_), mallow weed (_Malu_ sp. ), nutgrass (_Cyperus rotundus_), liver seed grass (_Urocunda panicoides_), and Bellive (_Ipomea pleleban_), were discriminated using hyperspectral data and were detected and analyzed using multispectral images. Discriminant analysis (DA) was used to identify the most significant spectral bands in order to discriminate weeds from sorghum using hyperspectral data. The results demonstrated good separation accuracy for _Amarantulus macrocarpus_, _Urocunda panicoides_, _Malu_ sp., _Cyperus rotundus_, and _Sorghum bicolor_ ([PERSON] at 440, 560, 680, 710, 720, and 850 nm. Later, the multispectral images of these six bands were collected to detect weeds in the sorghum crop fields using object-based image analysis (OBIA). The results showed that the differences between sorghum and weed species were detectable using the six selected bands, with data collected using an unmanned aerial vehicle. Here, the highest spatial resolution had the highest accuracy for weed detection. It was concluded that each weed was successfully discriminated using hyperspectral data and was detectable using multispectral data with higher spatial resolution. weed classification; hyperspectral reflectance; discriminant analysis; weed species; weed mapping; site-specific weed management 202111435. 29 May 202116 July 2  
wavelengths are detected for each plant [10]. One of the spectral analysis methods used to discriminate between winter (wheat and clover) and summer (maize and rice) crops is linear discriminant analysis (LDA) [11]. For instance, the spectral signatures of wheat range from 350 to 712, 451 to 1562, and 1951 to 2349 nm. In comparison to wheat, clover has spectral signatures in the range of 727 to 1299 nm. The spectral signatures for maize are similar to those of rice and almost the same as wheat, at 350 to 713, 1451 to 1532, and 1951 to 2349 nm. It has been proven that each plant or crop has unique spectral signatures and wavelength bands. Many statistical analysis methods are used to process hyperspectral data, including stepwise linear discriminant analysis (SLDA), principal component analysis (PCA), support vector machine (SVM), random forest (RF), neural network (NN), and linear discriminant analysis (LDA) approaches. STEPWISE was created based on these approaches. The RF and SVM models were tested in terms of their hyperspectral data with feature selection using a successive projection algorithm (SPA). Based on the SPA selection, the results showed more than 90% classification accuracy for barmard grass, weedy rice, and rice using a SVM with six spectral bands (415, 561, 687, 705, 735, and 1007 nm) [12]. The neural network machine learning algorithm successfully classified herbicide-resistant weeds at 25% to 79% accuracy using an unmanned aerial vehicle (UAV) [13]. The results depended on the sunlight, images, and growth stage of the plants. Linear discriminate analysis (LDA) and support vector machine (SVM) classifiers are the best techniques for weed classification, with 90% and 87.14% accuracy, respectively [14]. The same goes for LDA in classifying lettuce varieties, providing 81.04% accuracy [15]. This shows that the plants' spectral signatures can be used for classification, which can be improved using UAV image processing. It is challenging to process the resulting images due to the similarities between weeds and crops; thus, object-based image analysis (OBIA) can be used to segment features based on the objects in an image. The combination of k-means and multiresolution segmentation OBIA can be used to differentiate weed species [16]. The segmentation is performed based on the textures, objects, and color features [16].