## 3 Results  
In this section, the practical application of the system is illustrated through a real-life example: the classification of strawberries by the presence of defects. Additionally, data collected from the utilization of this system in the mentioned SME is presented to provide insights into its effectiveness and impact in real-world scenarios. To demonstrate the efficacy of the developed system, a real-world case study is presented, focusing on the unripe strawberries (Class B) or the detection of strawberries exhibiting grey mold, rot, and mechanical damage (Class C). The performance of various YOLOv8 and YOLOv9 models on detecting strawberry defects is presented in Table 2a,b, respectively. The results reveal distinct differences in model complexity, speed, and detection accuracy. The YOLOv8m model, with 168 layers and 3,006,038 parameters, achieved high precision (\(p\)) of 0.828 and recall (r) of 0.957, resulting in a mean average precision (mAP) of 0.945 at 50% IoU and 0.931 across IoUs ranging from 50% to 95%. The YOLOv8s model, also with 168 layers but significantly more parameters (11,126,358), exhibited slightly higher precision at 0.854 but lower recall at 0.863, achieving a mAP of 0.942 and mAP50-95 of 0.92. The YOLOv8m model, with 218 layers and 25,840,918 parameters, demonstrated a balanced performance with a precision of 0.826 and recall of 0.878, yielding a mAP of 0.896 and mAP50-95 of 0.87. The YOLOv8l model, with 268 layers and 43,608,150 parameters, showed a significant drop in precision to 0.497 but maintained a very high recall of 0.992. This model achieved a mAP of 0.601 and a mAP50-95 of 0.588. Lastly, the YOLOv8s model, with the highest complexity of 268 layers and 68,125,494 parameters, had a precision of 0.536 and recall of 0.933, with a mAP of 0.581 and mAP50-95 of 0.554. The calculated F1 scores reveal that the smaller YOLOv8 models (n, s, and m) consistently demonstrate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**(a)**} \\ \hline
**Metric** & **YOLOv8 n** & **YOLOv8 s** & **YOLOv8 m** & **YOLOv8 l** & **YOLOv8 x** \\ \hline \(p\) & 0.828 & 0.854 & 0.826 & 0.497 & 0.536 \\ \hline r & 0.957 & 0.863 & 0.878 & 0.992 & 0.933 \\ \hline mAP & 0.945 & 0.942 & 0.896 & 0.601 & 0.581 \\ \hline mAP50–95 & 0.931 & 0.920 & 0.870 & 0.588 & 0.554 \\ \hline F1 score & 0.888 & 0.859 & 0.851 & 0.661 & 0.679 \\ \hline layers & 168 & 168 & 218 & 268 & 268 \\ \hline parameters & 3,006,038 & 11,126,358 & 25,840,918 & 43,608,150 & 68,125,494 \\ \hline FLOPs (B) & 8.7 & 28.6 & 78.9 & 165.2 & 257.8 \\ \hline \multicolumn{5}{c}{**(b)**} \\ \hline
**Metric** & **YOLOv9 m** & **YOLOv9 c** & **YOLOv9 l** & **YOLOv9 s** & **YOLOv9 e** \\ \hline \(p\) & 0.761 & 0.813 & 0.832 & 0.831 & 0.498 \\ \hline r & 0.899 & 0.936 & 0.889 & 0.877 & 0.997 \\ \hline mAP & 0.918 & 0.931 & 0.938 & 0.934 & 0.597 \\ \hline mAP50–95 & 0.902 & 0.906 & 0.92 & 0.913 & 0.582 \\ \hline F1 score & 0.824 & 0.870 & 0.860 & 0.853 & 0.664 \\ \hline layers & 374 & 384 & 917 & 486 & 687 \\ \hline parameters & 20,014,438 & 25,320,790 & 2,005,798 & 7,167,862 & 57,377,942 \\ \hline FLOPs (B) & 76.3 & 102.1 & 7.7 & 26.4 & 189.0 \\ \hline \end{tabular}
\end{table} 
Table 2: (**a**) Summary of key performance metrics for YOLOv8 Models. (**b**) Summary of key performance metrics for YOLOv9 Models. higher harmonic mean values of precision and recall, indicating better overall performance and balance in detecting and correctly identifying strawberry defects compared to the larger YOLOv8 models (l and x), which show lower F1 scores despite their higher computational complexity. Regarding computational speed on CPU using ONNX, YOLOv8 x emerges as the slowest among the variants, requiring considerable processing time. This characteristic suggests potential challenges in applications where rapid detection is necessary. The disparity in model parameters and FLOPs across YOLOv8 n to YOLOv8 x highlights trade-offs between model complexity and computational efficiency.