###### Abstract  
Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis. rice plant; weed; hyperspectral imagery; remote sensing +
Footnote †: journal: Medical Imaging  
**The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2,**  
**[PERSON] 3, [PERSON] 4,**  
**[PERSON] 1,**  
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[PERSON] [19] stated that plants' spectral reflectance is identified based on the cellular and biochemical leaf structure and leaf canopy. Figure 3 represents a typical spectrum reflectance and transmitted wavelength of green leaf. The contrast of the reflectance and transmittance spectra depend upon absorption which is in the visible spectrum range 400-700 nm, the spectra are controlled by absorption of various pigments and primarily chlorophylls. In near infra-red (NIR), the reflectance spectra are high which is close to 50% and flat while above 1300 nm, the reflectance declines because of the water absorption present in the leaf. Further contemplation in the vegetation mapping procedure is the size of the objects to be mapped. Higher spatial resolution imagery is frequently used for mapping narrow vegetation objects which are acquired from airborne sensors [60]. Weeds compete with crops and are difficult to distinguish because of their similar colour, shape, and size [61]. However, a previous study on dispersing reflectance spectra of crop and weed leaves found the potential of weed detection with reflectance measurements. [PERSON] et al. [62] mentioned that due to the considerable absorption by chlorophylls, a plant leaf typically has a low reflectance in the visible spectral range and a comparatively high reflectance in the near-infrared spectral area due to internal leaf scattering and no absorption. Therefore, according to [PERSON] et al.