# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 3 Results and Discussion
Among YOLOv8 model scales, YOLOv8k obtained the best P and R values (0.95463 and 0.9761, respectively) while YOLOv8l resulted with the best mAP_0.5 and mAP_0.5:0.95 (0.9775 and 0.8129, respectively). Based on these results, YOLOv5m, YOLOv6l, YOLOv7, and YOLOv8l were tested on the four different test datasets. The results of this trial are reported in Table 4.  
Table 4 shows the results of the YOLO model's detection on the four test datasets (confusion matrix is reported in Table A1 of Appendix A). All the tested models obtained higher performances on the 'Weeds' public dataset than the additional test datasets. For this test, YOLOv8l resulted with the highest P value (0.9476), best mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123). YOLOv5m resulted in the highest R (0.9663). The time required for models to inference on this test dataset was approximately 34 ms per image for YOLOv8l and 16.2 ms for YOLOv5m. When performing inference on the Home Lawn dataset, YOLOv6l resulted with the best P (0.7836), while YOLOv7 obtained the best R (0.6454), best mAP_0.5 (0.7108), best mAP_0.5:0.95 (0.5209). The inference time required for this dataset was approximately 32.5 ms for YOLOv6l and 29.1 ms for YOLOv7. The best models performing inference on the Baseball Field dataset were YOLOv5m with the best P (0.6856), \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **P** & **R** & **mAP\_0.5** & **mAP\_0.5:0.95** & **Inference (ms) 1 ** \\ \hline \multirow{4}{*}{EfficientDet} & \textasci{-}[3.0 pt] \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \textasci{-}[3.0 pt] \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 0.9273 & 0.9426 & 0.7023 & 44.3 \\  & Home Lawn & 0.5982 & 0.5149 & 0.5195 & 0.4155 & 52.0 \\  & Baseball Field & 0.6138 & 0.7069 & 0.6614 & 0.4136 & 54.2 \\  & Manila grass & 0.6047 & 0.6954 & 0.5691 & 0.4369 & 50.1 \\ \hline \multirow{4}{*}{YOLOv5m} & \textasci{-}[3.0 pt] \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \textasci{-}[3.0 pt] \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 0.9433 & 0.9663 & 0.9772 & 0.7828 & 16.2 \\  & Home Lawn & 0.6331 & 0.5272 & 0.5399 & 0.4263 & 19.2 \\  & Baseball Field & 0.6856 & 0.8126 & 0.7135 & 0.4716 & 24.1 \\  & Manila grass & 0.6441 & 0.5433 & 0.6412 & 0.5007 & 18.7 \\ \hline \multirow{4}{*}{YOLOv6l} & \textasci{-}[3.0 pt] \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \textasci{-}[3.0 pt] \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 0.9442 & 0.9494 & 0.9747 & 0.7612 & 22.8 \\  & Home Lawn & 0.7836 & 0.6446 & 0.7057 & 0.5022 & 32.5 \\  & Baseball Field & 0.6098 & 0.6491 & 0.5379 & 0.4108 & 47.9 \\  & Manila grass & 0.5865 & 0.7571 & 0.7014 & 0.5248 & 26.6 \\ \hline \multirow{4}{*}{YOLOv7} & \textasci{-}[3.0 pt] \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & 0.9265 & 0.9627 & 0.9745 & 0.7685 & 16.1 \\  & Home Lawn & 0.7118 & 0.6454 & 0.7108 & 0.5209 & 29.1 \\ \cline{1-1}  & Baseball Field & 0.6223 & 0.7579 & 0.6379 & 0.4009 & 35.6 \\ \cline{1-1}  & Manila grass & 0.6549 & 0.7571 & 0.6461 & 0.4614 & 27.5 \\ \hline \multirow{4}{*}{YOLOv8l} & \textasci{-}[3.0 pt] \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & 0.9476 & 0.9610 & 0.9795 & 0.8123 & 34.0 \\  & Home Lawn & 0.6567 & 0.6422 & 0.6564 & 0.4721 & 37.4 \\ \cline{1-1}  & Baseball Field & 0.6672 & 0.6474 & 0.6459 & 0.4312 & 19.7 \\ \cline{1-1}  & Manila grass & 0.7635 & 0.6519 & 0.7589 & 0.5296 & 36.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance and inference time of best YOLO model scales for the four studied YOLO versions (YOLOv5m, YOLOv6l, YOLOv6l, YOLOv7, and YOLOv8l) and EfficientDet. best R (0.8126), mAP_0.5 (0.7135) and best mAP_0.5:0.95 (0.4716). The inference time required was approximately 24.1 ms for YOLOv5m. For the Manila grass dataset, the best P was obtained from YOLOv8l (0.7635), and the best R was obtained from YOLOv7 and YOLOv6l (both models got R of 0.7571). YOLOv8l resulted in the highest mAP_0.5 and mAP_0.5:0.95 (0.7589 and 0.5296, respectively). [PERSON] et al.