# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **Precision** & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**mAP\_0.5**} & \multicolumn{2}{c}{**mAP\_0.50.95**} \\ \hline EfficientDet & 0.9033 \(\pm\) 0.0244 \({}^{*}\) & b ** & 0.8862 \(\pm\) 0.0312 & c & 0.8931 \(\pm\) 0.0312 & d & 0.7172 \(\pm\) 0.0313 & ef \\ \hline YOLOv5n & 0.9259 \(\pm\) 0.0102 & ab & 0.893 \(\pm\) 0.0594 & bc & 0.9343 \(\pm\) 0.0387 & abcd & 0.7002 \(\pm\) 0.012 & f \\ YOLOv5s & 0.9445 \(\pm\) 0.0281 & a & 0.756 \(\pm\) 0.0233 & de & 0.9104 \(\pm\) 0.0408 & bcd & 0.8674 \(\pm\) 0.0664 & a \\ YOLOv5m & 0.9305 \(\pm\) 0.0484 & ab & 0.7305 \(\pm\) 0.0381 & e & 0.9057 \(\pm\) 0.0307 & cd & 0.8841 \(\pm\) 0.0795 & a \\ YOLOv5l & 0.9264 \(\pm\) 0.0484 & ab & 0.7313 \(\pm\) 0.0672 & e & 0.9197 \(\pm\) 0.0333 & abcd & 0.8606 \(\pm\) 0.0442 & a \\ YOLOv5x & 0.939 \(\pm\) 0.0459 & ab & 0.7928 \(\pm\) 0.0181 & d & 0.9104 \(\pm\) 0.06 & bcd & 0.8789 \(\pm\) 0.0507 & a \\ \hline YOLOv6n & 0.9456 \(\pm\) 0.0146 & a & 0.7594 \(\pm\) 0.0172 & de & 0.9539 \(\pm\) 0.0142 & ab & 0.7112 \(\pm\) 0.0093 & ef \\ YOLOv6n & 0.9331 \(\pm\) 0.0294 & ab & 0.7707 \(\pm\) 0.0223 & de & 0.9392 \(\pm\) 0.0286 & abc & 0.7208 \(\pm\) 0.0186 & ef \\ YOLOv6m & 0.9401 \(\pm\) 0.0237 & ab & 0.7344 \(\pm\) 0.0293 & e & 0.9237 \(\pm\) 0.0354 & abcd & 0.7156 \(\pm\) 0.0192 & ef \\ YOLOv6l & 0.9414 \(\pm\) 0.023 & a & 0.7728 \(\pm\) 0.0346 & de & 0.9508 \(\pm\) 0.0147 & ab & 0.7277 \(\pm\) 0.0178 & def \\ \hline YOLOv7 & 0.9111 \(\pm\) 0.0209 & ab & 0.9552 \(\pm\) 0.0136 & a & 0.9594 \(\pm\) 0.0214 & a & 0.7625 \(\pm\) 0.014 & bcde \\ YOLOv7x & 0.9398 \(\pm\) 0.0238 & ab & 0.9338 \(\pm\) 0.0321 & ab & 0.9366 \(\pm\) 0.0302 & abcd & 0.7393 \(\pm\) 0.0244 & cdef \\ \hline YOLOv8n & 0.9266 \(\pm\) 0.0265 & ab & 0.9269 \(\pm\) 0.0271 & abc & 0.9412 \(\pm\) 0.0202 & abc & 0.7547 \(\pm\) 0.0403 & bcde \\ YOLOv8s & 0.9169 \(\pm\) 0.0275 & ab & 0.939 \(\pm\) 0.0526 & ab & 0.9051 \(\pm\) 0.0625 & cd & 0.7517 \(\pm\) 0.043 & bcdef \\ YOLOv8m & 0.9227 \(\pm\) 0.0257 & ab & 0.9247 \(\pm\) 0.0363 & abc & 0.9385 \(\pm\) 0.0326 & abc & 0.7769 \(\pm\) 0.0302 & bcd \\ YOLOv8l & 0.9235 \(\pm\) 0.0276 & ab & 0.9244 \(\pm\) 0.0417 & abc & 0.955 \(\pm\) 0.0263 & a & 0.8043 \(\pm\) 0.015 & b \\ YOLOv8x & 0.9304 \(\pm\) 0.021 & ab & 0.9189 \(\pm\) 0.0417 & abc & 0.9477 \(\pm\) 0.0296 & abc & 0.7925 \(\pm\) 0.0333 & bc \\ \hline \hline \end{tabular} \({}^{*}\) Values refer to the mean and standard deviation of models performance on five-fold cross validation. ** Different letters on the same column represent different values at \(p<0.05\).  
\end{table} 
Table 3: Results on five-fold cross validation test of different models studied. Figure 3: Results of precision (F), recall (R), mean average precision at 0.5 confidence levels (mAP_0.5), and mean average precision at 0.5–0.95 confidence levels (mAP_0.5–0.95) of best YOLO model scales  
for the four studied YOLO versions trained on the 'Weeds' public dataset for 100 epochs. (**a**) Different model scales Precision values for 100 epochs; (**b**) Different model scales Recall values for 100 epochs; (**c**) Different model scales mAP_0.5 values for 100 epochs; (**d**) Different model scales mAP_0.5:0.95 values for 100 epochs. In general, YOLOv6 model scales depicted a different trend during the train process since this model provide evaluation at the first epoch, the 20 th, the 40 th, and every three epochs starting from the 50 th. Among YOLOv5 model scales, YOLOv5s obtained the highest P (0.9485), while YOLOv5m obtained the best R (0.9761), mAP_0.5 (0.9783) and mAP0.5:0.95 (0.7811). Among the YOLOv6 models, YOLOv6n obtained the best P (0.9646) and best mAP_0.5 (0.9651), while YOLOv6l resulted with the best R (0.8893) and best mAP_0.5:0.95 (0.7421). YOLOv7 surpassed YOLOv7x for all the metrics resulting in P (0.9466), R (0.9663), mAP_0.5 (0.9758), and mAP_0.5:0.95 (0.7672).