# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 4 Conclusions  
The task of achieving satisfactory weed detection in turfgrass through training digital images poses significant challenges. In this study, different YOLO object detectors were prepared and tested for weed detection in turfgrass, considering different weeds species as a single class ('Weeds'). Among the tested models, YOLOv8l demonstrated the highest overall performance on the test dataset, achieving a precision of 0.9476, mAP_0.5 of 0.9795, and mAP_0.5:0.95 of 0.8123. Despite YOLOv8l high performances, results on the additional test datasets were not acceptable for a professional use. Consequently, it became evident that several obstacles hinder accurate weed detection, emphasizing the need for more in-depth research. To enhance performance, future investigations should focus on exploring weed detection algorithms that incorporate multiple vegetative indices and features. Additionally, alternative annotation techniques, such as instance segmentation, should be compared with the more conventional bounding box-based object detection to determine whether different techniques can potentially yield improvements in weed identification. Moreover, a broad spectrum of weed species and ecotypes should be included in the training and testing of weed detection algorithms to ensure accurate performance in turfgrass scenarios. In conclusion, the findings of this study underscore the challenges associated with weed detection in turfgrass using digital image training. Further research endeavors are imperative to address the identified limitations and advance the field of weed detection in turfgrass through the exploration of enhanced algorithms, annotation techniques, and broader inclusion of weed species and ecotypes. Conceptualization, M.S. ; methodology, M.S. and O.E.A.-A. ; software, M.S. and O.E.A.-A. ; validation, C.F., A.P. and M.F. ; formal analysis, M.S. ; investigation, M.S. ; resources, M.R. and A.P. ; data curation, M.S. ; writing--original draft preparation, M.S. ; writing--review and editing, O.E.A.-A. and M.P.-R.; visualization, M.F. ; supervision, O.E.A.-A. and M.P.-R.; project administration, C.F. and M.F. ; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The 'Weeds' dataset used for the training process is available in a publicly accessible repository [54]. The authors declare no conflict of interest. Figure 5: YOLO models and EfficientDet mean average precision (mAP) and inference time trade-off. Data were averaged among the four test datasets. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Weed Detection to Weed Recognition: Reviewing 50 Years of Research to Identify Constraints and Opportunities for Large-Scale Cropping Systems. _Weed Technol._**2022**, _36_, 741-757. [CrossRef]
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