## 2 Literature Review  
This section provides an extensive review of machine learning techniques used for the development of weed detection and classification systems. Many cutting-edge deep learning models for weed classification, such as VGG-16, CNN, NN, and random forest regression, have been proposed [29; 30; 31; 32]. [PERSON] et al. proposed a system for weed detection and image processing. The convolutional neural network (VGG16) was used, which shows 99.5% precision while classifying leaf species. The system generated good results, but there is room for development. Plant identification can be improved with a more robust algorithm that can distinguish more species of leaves regardless of color or form [29]. To identify weed locations in the field for precise killing without damaging the crops, a faster RCNN network, an object detection model, was applied by [PERSON] et al. for distinguishing weed and maize seedlings. The model detected weeds with 97.71% precision under three conditions: full-cycle, multi-weather and multi-angle. The model can only be used on maize farms, which limits its application. Furthermore, the detection speed of Faster RCNN was 7 frames per second, which is low for real-time weed detection [33]. YOLO is a one-stage approach for detecting objects. It improves object detection speed by performing a CNN architecture on the image to determine the position and type of the objects in the image. The first YOLO version was introduced by [PERSON] et al. in 2015 [34]. Many improved versions of YOLO, such as YOLOv1, YOLOv2, YOLOv3, YOLOv4, and YOLOv5, have been developed in recent years [35]. There are various improvements in architecture from YOLOv1 to YOLOv5. The first version of YOLO was detected through grid division but had low confidence. YOLO2 works with k-means anchor boxes. Whereas YOLO3 uses the feature pyramid network (FPN), YOLO4 is added with the generalized intersection over union (GIOU) loss function, the MISH activation function, and data enhancement through the mosaic mixup method [35]. YOLO5 is distinct from all previous releases. The most significant enhancements are mosaic data augmentation and auto-learning bounding box anchors [34]. Previous versions of YOLO have been extensively studied for application in weed detection. [PERSON] et al. implemented a deep weed detector/classifier for precision agriculture using the YOLOv2 fused with the ResNet-50 object detection model. Except for the sedge weed, their model achieved precision and recall of over 94% for each weed class. As they used an older version of YOLO2, it lacks an auto-learning bounding box anchor [36]. [PERSON] et al. compared three one-staged object detection models: YOLO-V4, YOLO5, and SSD MobileNet V2. The dataset consisted of 153 RGB images of onion plants. According to the study, YOLO5 performed significantly well. It consumed significantly fewer resources, making it suitable for real-time weed detection. At 0.195 mAP, it showedless mean inference time of 7.72 milliseconds as compared to the other two models. This study demonstrated that up-sizing the data through sample augmentation will produce better results [37]. In 2022, [PERSON] et al. evaluated three cutting-edge CNN-based architectures, You Only Look Once-v3 (YOLO3), CenterNet, and Faster R-CNN, for bok choy, also known as Chinese white cabbage, detection. The most accurate model for vegetable recognition was YOLO3, which had the highest F1 score of 0.971 as well as high precision and recall values of 0.971 and 0.970, respectively. YOLO3 had a similar inference time to CenterNet, but was substantially faster than Faster R-CNN. Overall, YOLO3 had the best accuracy and computational efficiency of the deep-learning architectures studied [38]. [PERSON] et al. compared the performance of the SSD model with the faster RCNN in 2020. The dataset contained UAV images of weeds collected from mid to late-season soybean fields. The models were evaluated based on values of mean intersection over union (IoU) and inference speed.