## 3 Results  
### Performance and Comparison of the Proposed Model  
The performance parameters, precision, recall, and F1 score of the improved YOLOv8 model and other models under the input conditions of img_size 640 and img_size 1280 are shown in Table 4. The recognition results of the proposed model are shown in Figure 11.  
For all the models used in the experiment, when img_size was 1280, there was a significant improvement in precision, recall, and F1 score, compared to what was obtained when img_size was 640. The average precision, recall, and F1 score had increased. Compared with the parameters between different models, the precision of the improved YOLOv8 model was 93.4, which was higher than those of other models. Its recall was 94, higher than those of most models but, however, 3 lower than the 97 of the YOLOv7 model. The F1 score value of the proposed model was 93.69, which was 25.42, 35.59, 12.05, 8.16, 18.45, 5.91, 32.36, and 8.65 higher than those of other models, respectively. For the detection results shown in Figure 11, the improved YOLOv8 model could accurately detect and distinguish strawberry targets at different growth stages in the image.  
The [EMAIL_ADDRESS] value change of the improved YOLOv8 model compared to other models during the training process is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Parameters (img\_size 640)**} & \multicolumn{3}{c}{**Parameters (img\_size 1280)**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline
1 & YOLOv3 & 71.97 & 52.06 & 60.42 & 81.00 & 59.00 & 68.27 \\
2 & YOLOv4 & 51.64 & 52.94 & 52.28 & 62.71 & 54.13 & 58.10 \\
3 & YOLOv5n & 71.80 & 72.00 & 71.90 & 77.70 & 86.00 & 81.64 \\
4 & YOLOv7 & 74.10 & 79.00 & 76.47 & 76.50 & 97.00 & 85.53 \\
5 & YOLOX & 75.51 & 41.14 & 53.26 & 83.97 & 68.15 & 75.24 \\
6 & YOLOv8n & 82.60 & 49.00 & 61.51 & 86.60 & 89.00 & 87.78 \\
7 & SSD-vgg16 & 59.65 & 22.25 & 32.41 & 75.00 & 51.87 & 61.33 \\
8 & Faster-RCNN & 72.60 & 83.10 & 77.49 & 77.70 & 93.90 & 85.04 \\
9 & Proposed Model & 79.00 & 85.00 & 81.89 & 93.40 & 94.00 & 93.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results of different models.  
Figure 11: Detection results of the proposed model.  
The red dots represent the positions with the highest [EMAIL_ADDRESS] values on each curve, and the red arrows connect the points with their [EMAIL_ADDRESS] values. The [EMAIL_ADDRESS] value of the proposed model gradually increased during the training and stabilized after reaching 200 epochs. Compared to the SSD-vgg curve, the improved YOLOv8 curve and the YOLOv5n curve exhibited a more stable upward trend. The optimal [EMAIL_ADDRESS] value of the proposed model was 84.2, which was 18.6, 14.4, 6.8, 2.9, 3.7, 5.1, 12.3, and 25.1 higher than those of the models of YOLOv3, YOLOv4, YOLOv5n, YOLOX, YOLOv8n, Faster R-CNN, and SSD-vgg16, respectively.  
The scatter plot between the detection speed and accuracy of different models is shown in Figure 13. The detection speed of the improved YOLOv8 model was 6.2 ms, slightly higher than the YOLOv5n model's 5.7 ms, but, however, faster than other models. The improved YOLOv8 model had the highest [EMAIL_ADDRESS] value. The detection speed could indirectly reflect the size of the model. The proposed model used the VOV-GSCSP module to optimize the C2f module in the neck part of the traditional YOLOv8 model. The module simplified the complexity of convolution operations and reduced model volume, while maintaining a high degree of accuracy.