## 3 Results and Discussion
### Weed Map Analysis  
The results show that the spatial resolutions of the aerial images affect the classification accuracy. The higher the spatial resolution (5.42 mm), the higher the accuracy (Figure 5). Most of the weeds were detected with a 5.42 mm resolution. Nevertheless, a spatial resolution of 20.31 mm can still be used to detect weeds in sorghum fields. It was concluded that weeds can be detected in a sorghum field at altitudes of 10 to 40 m.  
The six bands used were 850, 720, 710, 680, 560, and 440 nm (10 nm width). These six bands were based on the 2013 and 2014 datasets; however, not all weeds were detected in 2014. This is because different types of weeds were detected in 2014. This is why _Urocha  
Figure 5: Mosaic images at 5.42 mm (altitude: 10 m) and 20.31 mm spatial resolutions (altitude: 37.5 m). Adapted from [14].  
panticoides_ (LS), _Cyperus rotundus_ (NG), _Portulaca olerca_ (PG), and _Ipomea plebeian_ (B) were misclassified in some quadrants.  
In this study, image processing was carried out using rule sets constructed using object-based image analysis (OBIA). The rule sets are flexible and can be modified to obtain optimal results. The rule sets have to be modified to accommodate plant growth stages and image environments [20]. In this study, similar rule sets were used, although the scale, shape, and compactness settings were modified based on the spatial resolution of the images. The second group of segmentation settings depend on how much segmentation is achieved from the first-stage segmentation settings and how many more images need to be segmented.  
Multiresolution segmentation generates objects by merging several pixels together based on their homogeneity [44]; however, other algorithm techniques can be used to investigate the effectiveness of the segmentation. Since the classification process was focused on identifying weeds in the sorghum field, the soil (SO) classification was only of minor interest. In the future, the shadow objects could be separated to obtain more accurate results [45]. This also highlights the desirability of image collection when there are fewer shadows (before 12:00 p.m.).  
Image analysis could be enhanced using the object-based image analysis (OBIA) automatic processing technique. The geographic object-based image analysis (GEODIA) automatic classification technique has the potential for large-area processing and might be useful in the automatic processing of multiple images [20; 46]. This could be an alternative for detecting crop rows before identifying weeds within the crops [47]. This is possible because crop rows are planted at fixed locations and weeds tend to grow randomly within the fields.  
It is important to look at how objects are sampled to obtain unbiased and accurate results [48]. Since this study only focused on three main classes of objects, the use of KHAT statistics was acceptable. Kappa coefficients are frequently used to indicate classification accuracy and are more accurate than using percentages [49].  
Six spectral bands were used in this analysis. It might be useful to choose fewer bands or a combination of bands in future studies. For example, six bands were used in a previous study for segmentation analysis of weed detection [50]; however, these were narrowed down to calculate the normalized difference vegetation index (NDVI) bands, so as to discriminate between vegetation and soil.  
The detection accuracy can be improved by using hyperspectral imaging, as proposed by [41]. Our study used only six bands, while hyperspectral imaging involves of up to 1000 much narrower bands (depending on the sensor). Nevertheless, the cost is higher when hyperspectral imaging is used compared to using a multispectral camera; thus, for the multispectral bands, the use of three to six bands to detect weeds in fields is suitable and affordable.