## 3 Results and Discussion
### Band Identification  
The results showed that the highest accuracy for the six combinations was 93%, involving the combination of the bands at 440, 560, 680, 710, 720, and 850 nm. The linear discriminant analysis (LDA) results from the hyperspectral data were significant for the six combinations. Table 5 shows the results from the classification of band combinations based on Table 2. There were 28 combinations (please refer to [14]), although only three are highlighted in in Table 5. The band combinations were narrowed down into five-, four-, and three-band combinations. The combination results can be found in [14].  
The spectral signatures showed that each plant had significant bands. Based on the spectral signatures, each weed species in the sorghum field was identified. The band combinations helped us to more accurately identify the weeds in the sorghum field. The 20 significant bands were narrowed down to eight bands, which were used in the multispectral sensor. This method was similar to the previous research, in which the bands underwent further analysis [35]. The discrimination accuracy between sugar beets and maize was 90% when using specific spectral bands [36].  
A similar study used spectral signatures (500-550, 650-750, 1300-1450, and 1800-1900 nm) to successfully discriminate weeds from sugarcane [30; 32]. The short-wave infrared bands (SWIR; 1660, 1890, and 2000 nm) matched the primary features of the pure cellulose and lignin spectra of the plants. Changes in cellulose and leaf water content determine the levels of these characteristics, which can be leveraged to classify plant species [30]. Several wavelengths, including 491, 541, 641, 722, 772, 852, 942, 1047, 1132, 1443, and 2475 nm, were selected and calculated for the purpose of identifying plants species using an artificial neural network (ANN) and support vector machine (SVM) [31]. We found that ANN and SVM provided high levels of accuracy of up to 86%.  
Other examples were the wavelengths of 440, 500, 530, 550, 560, 575, 590, 620, 640, and 675 nm, which were used to classify seagrass in Southeastern Australia [2]. Similar results were found using specific wavelengths to discriminate _Bernudagrass_ (Tifway 419). In fact, wavelengths of 353, 357, 360, 362, 366, 372, 385, 389, 391, 396, 405, 441, 442, 472, 726, 727, 732, and 733 nm were found in the discriminant analysis (DA) of _Bernudagrass_ (Tifway 419) at 98% accuracy.  
The combination of visible (VIS) and near infra-red (NIR) bands improved the classification accuracy using linear discriminant analysis (LDA) between weeds and sugar beets (686, 694, 726, 856, 897, and 970 nm), as well as between potatoes and weeds (686, 726, 856, 897, 970, and 978 nm) [37]. Furthermore, the health of the citrus crop was verified using 440, 560, 680, 710, 720, and 850 nm bands [38], consistent with the results in this study.  
Mediterranean plants were found by using the high spectral resolution of the mid-infrared (MIR) radiation, enabling accurate species discrimination [39]. Since this study focused on the visible to near-infrared (325 to 1075 nm) range, we suggest that future studies should consider spectral reflectance from 1055 up to 2000 nm for further analysis of weed discrimination, because MIR reflectance is also affected by the moisture content of plants [39].