## 7 Direction for Future Work and Conclusions  
In most situations, removing weeds in agricultural areas requires the use of large amounts of chemical pesticides, which are damaging to the environment regardless of how effective they are at enhancing crop output. Precision spraying might be explored to optimise herbicide application in crop fields, thanks to recent advances in image sensors. In this paper, we have reviewed the situation of weeds in rice crops, the background of hyperspectral imaging and techniques for processing hyperspectral data. This study is interdisciplinary and experts from various disciplines, such as agronomy (weed science), remote sensing, computing, and engineering are collaborating. Hyperspectral remote sensing technology is an important component in precision farming and is being used by a growing number of scientists and agricultural researchers. The capacity to properly and reliably distinguish weeds from crops is a vital step in controlling or eradicating weed infestations in agricultural crops. Due to the abundance of spectral information sensitive to distinct plant biophysical and biochemical properties, hyperspectral imaging offers a lot of potential for applications in agriculture, especially precision agriculture. Hyperspectral remote sensing technology uses the difference in spectral reflectance qualities between weeds and crops to identify weeds in crop stands and aids in the compilation of weed maps in the field, allowing for the application of site-specific and need-based herbicides for weed management.  
Hyperspectral imaging data with high spatial resolution along with machine learning algorithms in remote sensing showed good potential in agricultural studies. In the recent decade, sensing technologies and machine learning approaches have grown at a breakneck pace. These advancements are expected to continue to provide more cost-effective and comprehensive datasets, as well as more advanced algorithmic solutions, allowing for better crop and environment status estimates and decision making. For more intricate hyperspectral picture classification, existing theories and algorithms still have some limitations. As a result, future research efforts will focus on developing more tailored hyperspectral image classification systems. Therefore, in order to successfully use the information on weeds and crop monitoring for economic benefit, a state or district level information system based on existing information on diverse crops produced from this hyperspectral remote sensing approach is required. Governments can use hyperspectral remote sensing data to make critical decisions about which policies to pursue and how to address agricultural concerns.  
Conceptualization, N.S. and N.N.C.; methodology, N.S. and M.H.M.R.; resources, N.S.; writing--original draft preparation, N.S.; writing--review and editing, N.S., N.N.C., A.S.J., N.M.N., W.F.F.I. and M.H.M.R.; visualization, N.S. and N.N.C.; supervision, N.N.C., N.M.N., M.H.M.R. and A.S.J.; project administration, A.S.J.; funding acquisition, A.S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Project entitled \"Pest and Disease Monitoring Using Artificial Intelligent for Risk Management of Rice Under Climate Change\" under the Long-term Research Grant Scheme (LRGS), Ministry of Higher Education, Malaysia, LRGS/1/2019/UPM/01/2/5 (vote number: 5545002).