## 4 Conclusions  
In this paper, we identified the significant spectral bands for weed discrimination using hyperspectral data, which can be used in multispectral sensors for weed mapping. We used discriminate analysis to produce six significant spectral bands for the weed classification process. We collected multispectral imagery of the study area using an MCA 6 camera and analyzed these images using object-based image analysis (OBIA). The hierarchical model of multiscale weed detection was used successfully to simplify the OBIA rule sets for weed mapping. We determined that the use of fewer bands and high spatial resolution improves weed classification. This demonstrates the importance of having optimum spectral and spatial resolutions. We also found that the growth stages during play an important role data collection for weed detection in sorghum fields. These findings will be significant in overcoming difficulties in weed detection and mapping for sorghum crops. The spectral signatures of weed species can be used as inputs in spectral libraries and as guidelines for accurate weed mapping. The findings can also be used as inputs to develop automated technology for accurate weed detection applications. This approach can be integrated with artificial intelligence (A.I) systems, tractors, unmanned aerial vehicles (UAV), or drones for weed detection and automated targeted spraying of herbicides. This approach could have a big impact on the agroecology environment and provide a cost-effective method for weed control management. Currently, the results of this study are limited to the analysis procedures used for the multispectral images of the weeds and sorghum plants collected in the study area. In future studies, we recommend using hyperspectral imaging and machine learning techniques at different growth stages, with different herbicide applications and over diverse areas. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spatial Resolution** & **Confusion Matrix** & **Weed (\%)** & **Soil (\%)** & **Sorghum (\%)** \\ \hline \multirow{4}{*}{5.42 mm} & Producer Accuracy (PA) & 81 & 100 & 90 \\ \cline{2-5}  & User Accuracy (UA) & 81 & 100 & 90 \\ \cline{2-5}  & Overall Accuracy (OA) & 92 & & \\ \cline{2-5}  & KHAT (K) & 97 & & \\ \hline \multirow{4}{*}{20.31 mm} & Producer Accuracy (PA) & 56 & 98 & 77 \\ \cline{2-5}  & User Accuracy (UA) & 61 & 95 & 77 \\ \cline{2-5}  & Overall Accuracy (OA) & 84 & & \\ \cline{2-5}  & KHAT (K) & 74 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix used for mosaic image resolution. Adapted from [14]. **Author Contributions:** Conceptualization, N.N.C., E.D., and M.G. ; methodology, N.N.C. and E.D. ; software, N.N.C. ; validation, N.N.C. and E.D. ; formal analysis, N.N.C. and E.D. ; writing--original draft preparation, N.N.C. ; writing--review and editing, N.N.C., E.D., and M.G. ; visualization, N.N.C. and E.D. ; supervision, M.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Ministry of Higher Education Malaysia and the University Putra Malaysia through scholarship funds provided to [PERSON]. **Acknowledgments:** I would like to thank the Ministry of Higher Education Malaysia and Universiti Putra Malaysia for granting me the opportunity to undertake my PhD at the University of Queensland. The utmost gratitude goes to [PERSON], [PERSON], [PERSON] (University of Southern Queensland), and [PERSON] for helping and guiding me in the statistical analysis. Additionally, I would like to thank [PERSON], [PERSON] (USQ UAV flight team), and [PERSON] (Tetracam Inc. Specialist) for their guidelines and assistance. The results presented here are from experiments conducted as part of a doctoral dissertation project for [PERSON] (2016). The dissertation is available at [[https://espace.library.uq.edu.au/view/UQ:406131](https://espace.library.uq.edu.au/view/UQ:406131)]([https://espace.library.uq.edu.au/view/UQ:406131](https://espace.library.uq.edu.au/view/UQ:406131)) (accessed on 1 July 2021).