### Algorithms and Modelling for Weed Detection Analysis
Second, the overlap of different plant components can make distinguishing weeds from crops difficult. According to [PERSON] et al. [76], most standard classifiers in machine learning are based on learning a discriminant function from labelled data (i.e., supervised learning). However, obtaining tagged data, as opposed to unlabelled data, can be time consuming and costly. [PERSON] et al. [77] stated that machine learning (ML) has risen to prominence with big data technology and high-performance computers to open up new avenues for unravelling, quantifying, and understanding data-intensive processes in agricultural operations. ML is characterised as a scientific subject that allows machines to learn without being strictly programmed, among other things. Examples of ML modelling include artificial neural networks (ANNs), Bayesian models (BM), deep learning (DL), dimensionality reduction (DR), decision trees (DT), ensemble learning (EL), instance-based models (IMB) and support vector machines (SVMs). [PERSON] et al. [18] investigated site-specific weed management in the rice field using two metaheuristic algorithms: The bee algorithm (BA) and particle swarm optimisation (PSO), in order to improve the neural network's ability to identify the most effective characteristics and classify different types of weeds. Because of their abundance in the chosen region, this study focused on a rice cultivar (_Turon Mahali_) and two common types of weeds (narrow-leaf weeds (_Echitochloa crus-Galli, Papalum distichum_, and _Cyperus diffornis_) and wide-leaf weeds (_Alisma plantago-aquatica_ and _Eclipta prostrata_) while a stereo camera was used to collect the necessary data in the form of stereo videos, with different channels of each frame extracted. The proposed stereo vision technique, which averaged the related points on various channels and the proposed hybrid ANN-BA classifier for better classification accuracy, proved to have promising capabilities. [PERSON] et al. [78] created and evaluated a new classification algorithm based on colour indices and support vector data description (SVDD). In the first, second and third years of a three-year case study, overall accuracies of 90.19%, 92.36%, and 93.8%, respectively, were achieved. [PERSON] et al. [79] looked at how to categorize paddy crops and weeds from digital images utilizing several classifier systems developed with support vector machines (SVM) and random forest classifiers (RFs) in which the dataset included paddy plants and weeds from the seedling stage (1-leaf seedling) to the flowering stage. The results with an accuracy of 91.36% showed that multiple classifier systems were shown to outperform single classifier systems and the extracted features are good for paddy crops and weeds classification. [PERSON] et al. [53] studied weed identification by using hyperspectral data images trained on three classification models, namely partial least squares discriminant analysis, support vector machine and multilayer perceptron (MLP) with an overall accuracy range of about 70-100%. The analysis was run by using the whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from four different weed samples which comprised two types of grass (_Setaria pumila_ [yellow bristle grass] and _Stipa ar arundinacea_ [wind grass]) and two broadleaf weed species (_Rannuculus acris_ [giant buttercup] and _Cirsium arvense_ [Californian thistle]). Results showed that using both Av and Sp spectra were able to identify the four weeds' species. To solve the challenge of forecasting the pre-planting risk of Stagonospora nodorum blotch (SNB) in winter wheat, [PERSON] et al. [80] used machine learning approaches such as artificial neural networks (ANNs), category and regression trees, and random forests (RFs). They created risk assessment models that could help with disease control decisions before planting the wheat crop. Research conducted by [PERSON] et al.