### Algorithms and Modelling for Weed Detection Analysis  
For various agricultural applications, several remote sensing approaches, such as hyperspectral data from airborne, satellite platforms using multispectral and optical imagery have been proposed [69; 70]. A Study conducted by [PERSON] et al. [71] looked into the drawbacks and benefits of using a combination of radar data and optical images to determine the types of crops in the Tarom region (Iran) in which the Sentinel 1 and Sentinel 2 images were utilised to generate a map for the selected research area. Hyperspectral sensing, which measures reflectance from visible to shortwave infrared wavelengths, has allowed vegetation to be classified and mapped at a variety of taxonomic scales, often down to the species level. To reduce the dimensionality of the data to a level suitable for the creation of a classification model, hyperspectral measurements recorded by narrowband spectroradiometers or imaging sensors have typically required some type of spectral feature selection [72]. Therefore, the remote sensing method can detect the existence of non-crop plants between rows, such as the recognition of weeds within rows, whereas segregating weeds from crops and identifying weed species emerging from proximal sensing research has utilized both spectral reflectance and leaf shape analysis for identification [73]. According to [PERSON] et al. [74], to examine the datasets generated by these methodologies, proper and effective sophisticated algorithms as well as high-power computation are required. Genetic programming was utilized by [PERSON] et al. [75] to distinguish between rice and other leaf groups. They also employed a scanning window of 20 \(\times\) 20 pixels on a test image to evaluate the classifier, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Crop/Weed Mixture** & **Reflectance** & **Wavelength (nm)** \\ \hline
**Wheat system** & & \\ Wheat (_Triticum aestivum_) & 0.42–0.45 & 720–850 \\ Broadleaved weeds & 0.6–0.8 & 720–850 \\ Grass weeds & 0.55–0.6 & 720–850 \\ Wheat stubbles & 0.2–0.3 & 1425–2250 \\ Wheat stubbles heavily grazed & 0.3–0.4 & 1425–2250 \\ Cruciferous weeds & 0.65–0.7 & 750–900 \\ Wheat & 0.4–0.5 & 720–900 \\ \hline
**Soybean system** & & \\ Soybean (_Glycine max_) & 0.85–0.90 & 750–900 \\ Prickly sidia (_Silta spinsa_) & 0.78–0.82 & 750–900 \\ Pitted morning glory (_Ipoma lacunosa_) & 0.6–0.65 & 750–900 \\ Sicklepod (_Cassia obtusifolia_) & 0.52–0.55 & 750–900 \\ \hline
**Cotton system** & & \\ Cotton (_Gossypium hirsutum_) & 0.48–0.52 & 400–600 \\ Cogongrass (_Imperata cylindrica_) & 0.5–0.6 & 400–750 \\ Johnsongrass (_Sorghum halepense_) & 0.45–0.52 & 400–750 \\ Sicklepod (_C. obtusifolia_) & 0.21–0.3 & 400–800 \\ \hline
**Sorghum system** & & \\ Sorghum (_Sorghum bicolor_) & 0.55–0.6 & 720–1000 \\ Common lampsquarters (_Clenopodium album_) & 0.39–0.41 & 720–1000 \\ Pigweed (_Amaranthus_ spp.) & 0.48–0.52 & 720–1000 \\ Barnyardgrass (_Ethchinolda_ spp.) & 0.35 & 72–1000 \\ Mallow (_Malva_ spp.) & 0.42–0.44 & 720–1000 \\ Purple nutsedge (_Coperus rotundus_) & 0.28–0.30 & 720–1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Published reports/study on crop and weed species classification using hyperspectral imagery [57]. attaining a 90% accuracy by applying the classifier to each pixel of the window based on a colour threshold. Therefore, several automatic classification techniques have been employed to classify remote sensing data and plant monitoring procedures, for example, the machine learning method [74]. According to [PERSON] et al. [18], machine vision based on image processing has been used to collect data in two different ways: Two-dimensional (2D) vision and three-dimensional (3D) vision. When using 2D cameras, machine vision systems based on two-dimensional (2D) image processing have some drawbacks. First, differences in external illumination have an impact on the quality of images captured by 2D cameras; thus, the camera's field of view must be covered.