# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 3 Results and Discussion
[18] trained and tested multiple species of two-stage detectors to detect different weeds among actively growing perennial ryegrass, obtaining higher values of R (>0.98). Low R values suggest that the model misclassifies target weeds as turfgrass, thus producing an FN. This is unacceptable for field applications since weeds would be missed, leading to unsatisfactory weed control in turfgrass. Figure 4 shows an example of YOLOv5m, YOLOv6l, YOLOv7, and YOLOv8l weeds detection on the test datasets. As shown in Figure 4, the model effectively detected weeds at a mature stage (>5 true leaves) growing outside from a turfgrass canopy. The training dataset consists of a multiple weed species dataset, most of which were rosette-forming weeds grow in turfgrasses. For this reason, the model showed high performances in a situation similar to those occurring in the public 'Weeds' dataset. These findings are in accordance with [20]. In this research, authors argued that the detection is highly affected by the broad-leaved weeds morphology and leaf pattern and color variations among species and within the same species. The authors proposed that multiple-species neural network training and images gathered from different geographical regions (to include various turf sites and weed biotypes) may be beneficial for the overall accuracy of weed detection models. However, the results obtained from experiments conducted on different datasets suggest that efforts are still required to improve the overall accuracy. [PERSON] et al. [58] proposed an improved YOLO architecture  
Figure 4: Example of weed detections using the _EfficientDet_, YOLOv5m, YOLOv6l, YOLOv7, and YOLOv8l models on the four test datasets (public ‘Weeds,’ Home lawn, Baseball Field, and Manila grass). that allows a more efficient detection of smaller objects. A more minor object detection was improved by modifying the architecture structure and fine-tuning parameters and resulted in an increased mAP_0.5 of approximately 7% without significantly affecting the inference time. Moreover, the models failed to detect weeds close to the image edge. [PERSON] et al. [19] claim that the edge effect may be reduced by the continuous frames inputs (since in field applications, weed detection is based on videos), thus boosting detection accuracy. In this trial, all the models were able to detect weeds at the edge of the image frames. Furthermore, highly complex backgrounds such as low-quality turf may increase the computational complexity for feature extraction and a reduced R of the model [19]. In this trial, only YOLOv5m models agree with this finding. Indeed, YOLOv5m obtained the lowest R when detecting weeds in the Home Lawn dataset, which had the most complex background. This limitation may be overcome by increasing the number of training images. [PERSON] et al. [28] obtained similar low P and R values when using YOLOv3 for R. scabra detection in drought-stressed and unstressed turfgrasses. In this research, authors argued that high background variability in the training dataset increases cause a less efficient feature extraction and consequently decreases P and R metrics. For this reason, the authors suggest further research on training object detectors on images with the simplest background. Additionally, the annotation method used in this trial consisted of drawing bounding boxes around the weed within the image, which is not the method with the highest resolution. Indeed, [PERSON] et al. [59] demonstrate that higher-resolution annotation methods improve the overall neural network accuracy. Moreover, artificial neural networks recognize plants using color, texture, and shape features [60], and [PERSON] et al. [61] found that multispectral components are highly effective in broadleaves weed detection in turfgrass. Thus, further research may be addressed to assess and clarify how these techniques and methods can improve detector's performances. According to [PERSON] et al. [62], high image processing speed is imperative for real-time weed detection and treatment.