## 3 Results and Discussion
### Results of Image Processing  
The same six spectral bands were used for image collection in the field. The spectral, physical, and contextual characteristics of the weeds and sorghum affect the development and efficiency of the segmentation and classification rule sets in image processing procedures. The rule sets in the object-based analysis (OBIA) can be modified to identify weeds at different growth stages so that the results are more accurate. In addition, the detection of weeds will be more accurate when using a high spatial resolution. The growth stages of the weeds from week one to week four after being planted were assessed via multispectral images. Based on the results, weeds and sorghum were accurately classified in week three (Table 6), because the sizes of both were bigger in comparison to weeks one and two; however, the size of the weeds was still small and they were easy to separate from the sorghum plants. The same was true for _Cyperus rotundus_ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Combination** & **The Band Combinations** & **AM** & **LS** & **MW** & **NG** & **Mean** \\
**Number** & **(nm)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 440, 560, 680, 710, 720, 730 & 43 & 100 & 100 & 100 & 86 \\ \hline
2 & 440, 560, 680, 710, 720, 750 & 43 & 100 & 100 & 100 & 86 \\ \hline
3 & 440, 560, 680, 710, 720, 850 & 71 & 100 & 100 & 100 & 93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results based on the band combination. Adapted from [14]. (NG) and _Urochoa panicoides_ (LG), which were successfully classified in week three after planting. The shapes of leaves such as broadleaf _Portulaca olerca_ (PG) and _Ipomea plebeian_ (B) were easier to distinguish. Both were successfully classified in week three. The literature showed how grass weed seedlings were discriminated from monocotyledonous crops, with seedlings of broadleaf weeds in dicotyledonous crops proving more challenging to identify at later stages [40] because they have similar spectral reflectance and due to the spread of weeds in small patches and the effects of soil reflectance at the early stages of growth [41]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image Height: 1.6 m (RGB)**} & **Week 3 (GSD: 10.83 mm)** & **Week 3 (GSD: 20.31 mm)** \\ \cline{2-3}  & **17 th December 2014** & **17 th December 2014** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Weed species at a ground sampling distances (GSDs) of 10.83 mm (altitude: 20 m) and 20.31 mm (altitude: 37.5 m) for week three. Adapted from [14]. The weeds started to mix with the crop at week four; thus, the broadleaf weeds were detected at an early stage. Weed detection earlier stages of growth is consistent with weed control strategies that are designed to minimize competition with sorghum plants, as weeds become difficult to detect at the later stages of growth. Additionally, the background reflectance in images also affects weed detection in the field. For example, the background reflectance of sorghum can be used in object-based image analysis (OBIA) to identify the shapes of weed leaves more accurately as compared to the sorghum background; therefore, the background was extracted as a black shade (with a low digital number) in the image analysis. This is consistent with the study by [42], who normalized red-green-blue (RGB) data into the hue, saturation, and intensity (HSI) image space to segment plant images against background regions. The HSI transformation is a standard technique used to numerically describe color in the image domain using spherical coordinates, which are roughly analogous to the conventional attributes of hue, saturation, and lightness (I) [43], whereby images are collected under artificial light. In contrast with our study, the images were collected under field illumination conditions.