## 4 Experiments and Analysis
### Comparative Experiments of Different Models  
To further verify the superiority of the SEM-YOLOv8n algorithm in this study, we carried out two comparative experiments. In the first experiment, we directly compared SEM-YOLOv8n with other versions of the YOLO series, aiming to validate the effectiveness and advancement of the proposed improvements, ensuring that, while retaining the key advantages of the YOLO series, the model's performance in mobile applications was further enhanced. The second experiment compared SEM-YOLOv8n against other traditional object detection algorithms, providing a comprehensive evaluation of its overall competitiveness in the broader object detection field.  
Figure 8: Confusion matrix.  
In the first experiment, models from the YOLO series, including YOLOv5n, YOLOv7-tiny [19], YOLOv8n, YOLOv9t [20], YOLOv10n, and YOLO11n, were selected for comparison. The experimental results are shown in Table 3.  
As seen in Table 3, compared to models such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv9t, YOLOv10n, and YOLO11n, the average accuracy values of the SEM-YOLOv8n model proposed in this paper are higher by 1.4, 2.5, 1.1, 12.3, 1.8, and 2.2 percentage points, respectively. Compared with the YOLOv5n model, although the improved model size and the number of parameters increased by 4.7% and 4.2%, the mean values of its recall and average precision were 3.4 and 1.4 percentage points higher, respectively. The larger number of parameters and the model size of YOLOv7-tiny are not favorable for deployment in resource-constrained environments, such as the mobile terminal. Additionally, the YOLOv9t, YOLOv10n, and YOLO11n models failed to outperform the SEM-YOLOv8n model in terms of the accuracy, recall, and average precision averages.  
In the second experiment, four exemplar object recognition algorithms, including SSD, Faster-RCNN, RetinaNet, and EfficientNet, were chosen for the comparative analysis. Table 4 presents the experimental findings.  
Table 4 illustrates the SEM-YOLOv8n algorithm's substantial benefits in pinpointing various levels of grape damage inflicted by _Brevipalpus_ spp. Ultimately, a computed average precision rate for these damages persevered, with the SEM-YOLOv8n algorithm surpassing all others in the identification accuracy, at 97.6%. This finding unequivocally confirms the efficacy and dominance of the enhanced SEM-YOLOv8n algorithm in categorizing grape damage caused by _Brevipalpus_ spp.  
In summary, the SEM-YOLOv8n model proposed in this study has a better detection performance and strikes a good balance between precision and efficiency with a smaller model size.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grade Damage** & **SSD (\%)** & **Faster-RCNN** & **RetinaNet (\%)** & **EfficientNet (\%)** & **SEM-YOLOv8n** \\ \hline Grade I damage & 89.86 & 83.96 & 91.03 & 86.87 & 97.8 \\ Grade II damage & 86.56 & 72.91 & 83.28 & 83.19 & 97.6 \\ Grade III damage & 86.82 & 68.95 & 78.07 & 81.72 & 95.5 \\ Grade IV damage & 93.18 & 71.43 & 89.78 & 81.26 & 98.3 \\ Grade V damage & 94.97 & 67.13 & 85.47 & 89.73 & 98.5 \\ All grades & 85.08 & 72.88 & 85.53 & 78.26 & 97.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _P/\%_ & _R/\%_ & _mAP/\%_ & **Parameter/M** & **Model Size/MB** \\ \hline YOLOv5n & 94.3 & 91.1 & 96.2 & 2.5 & 5.05 \\ YOLOv7-tiny & 91.9 & 90.8 & 95.1 & 6.1 & 11.7 \\ YOLOv8n (baseline) & 95 & 91.8 & 96.5 & 3.01 & 5.98 \\ YOLOv9t & 74 & 78.4 & 85.3 & 2.01 & 4.41 \\ YOLOv10n & 92 & 90.5 & 95.8 & 2.7 & 5.52 \\ YOLO11n & 92.6 & 90.3 & 95.4 & 2.6 & 5.24 \\ SEM-YOLOv8n & 94.8 & 94.5 & 97.6 & 2.61 & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the experimental results with the YOLO models.