### Algorithms and Modelling for Weed Detection Analysis
[81] combined the application of multi-feature fusion and support vector machine (SVM) to detect corn seedlings and weeds for limiting crop damage with an average recognition accuracy of about 97.50%. The dataset included a small database of corn seedlings and weed and actual field images. The results of the experiments revealed that the fusion feature of rotation invariant local binary pattern (LBP) feature and grey level-gradient co-occurrence matrix based on an SVM classifier accurately detected all types of weeds and corn seedlings. This provides information about weed and crop positions to the spraying herbicide, allowing for exact spraying and fertilising. [PERSON] et al. [82] used a wavelet packet transform paired with a weighted Bayesian distance based on crop texture and leaf data to identify the crop. The dataset needed for this study included field crop images captured with a digital camera with a resolution of 640 \(\times\) 480 pixels. To discriminate plants, they estimated energy coefficients in multiple frequency bands produced after the change. The crop identification achieved an accuracy of 94.63% by using the decision distance in different climates over three consecutive days of photography. [PERSON] and [PERSON] [83] used integrate decision tree (DT) and fuzzy logic techniques to establish a fuzzy model for differentiating the peanut plant from broadleaf weeds with the overall accuracies on training and testing datasets being, respectively, 92% to 96%. On the input dataset, two feature selection approaches were utilised: Principal component analysis (PCA) and correlation-based feature selection (CFS), and three decision trees (DTs) were used to distinguish between distinct plants: J48, random tree (RT), and reduced error pruning (REP). Another study by [PERSON] et al. [84] is on texture features recovered from wavelet sub-images to detect and describe four species of weeds in a sugar beet field, while neural networks (NN) were run as a classifier. Images were taken from sugar beet fields with a resolution of 96 \(\times\) 1280 pixels when the plants had six to eight leaves with significant occlusion and a height of about 80 mm to 160 mm. The research found that even at a stage of beet growth greater than six leaves, the application of wavelets proved to be effective for weed detection. Two-dimensional Gabor filters were employed to extract the features in a study conducted by [PERSON] et al. [85], and an artificial neural network (ANN) was utilized to categorize broadleaf and grass weeds. The seeds of the selected broadleaf weed species were planted and the image was captured four weeks after seeding. The Gabor wavelet/ANN system was created to use texture features to classify weed images into broadleaf and grass categories. Their findings revealed that joint space-frequency texture properties might be used to classify weeds. Furthermore, in agricultural research, deep learning combined with advancements in computer technology, particularly graphical processing units (GPU) embedded processors, has produced remarkable results for image classification and objection detection [86; 87]. According to [PERSON] et al. [88], deep learning (DL) algorithms have many advantages over traditional machine learning approaches for image classification, object detection and localization. To build a feature extractor from raw data, traditional machine learning techniques necessitate extensive domain knowledge [89; 90]. The DL approach, on the other hand, employs a representation-learning method in which a machine can automatically discover discriminative features from raw data for classification or object detection problems. DL methods can effectively extract discriminative features of crops and weeds due to their strong feature learning capabilities. Furthermore, as data sets have grown larger, the performance of traditional machine learning approaches has become saturated. When large datasets are used, DL techniques outperform traditional machine learning techniques [88]. [PERSON] et al.