## 4 Experiments and Analysis of Results
### Comparative Experiments  
In order to verify the effectiveness of the algorithm in this paper, it is experimentally compared with five mainstream target detection algorithms, namely, Faster R-CNN, SSD, YOLOv5, YOLOX, YOLOv7,YOLOv9, YOLOv12, DETR3D, and Swin Transformer, on the dataset of this paper. The principle of control variables is strictly followed in the experiments, and the experimental software, hardware, and settings are consistent [20]. The experimental results are shown in Table 3. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **YOLOv7-Only** & **U-Net-Only** & **Combined Model** \\ \hline [EMAIL_ADDRESS] & 95.35\% & 91.48\% & **96.91\%** \\ [EMAIL_ADDRESS] & 89.47\% & 90.12\% & **92.14\%** \\ Dice Coefficient & - & 88.23\% & **91.76\%** \\ F1-Score (per pixel) & - & 89.58\% & **92.45\%** \\ FPS & 87.48 & 58.32 & **90.41** \\ Recall & 94.73\% & 90.25\% & **97.31\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of model configurations. Figure 9: Detection method of the combined network of YOLOv7 and U-Net. Removing SE from YOLOv7 reduces [EMAIL_ADDRESS] by 1.8% while increasing FLOPs by 12%. Similarly, CBAM removal from U-Net degrades the Dice coefficient by 3.4% and increases inference time by 8%. These results validate that attention modules improve efficiency while avoiding redundancy. The algorithm in this paper achieves detection accuracy compared to other mainstream target detection algorithms in terms of precision, recall, and mean average precision (mAP), where the precision is 97.49%, the recall is 97.31%, and the mAP is 96.91%, which is an improvement of 1.56% compared to the baseline algorithm; although the YOLOv5 and YOLOX algorithms significantly outperform the SSDs in terms of detection accuracy and speed, there is still a gap between the algorithms in YOLOv5 and YOLOX and this paper. Although the YOLOv5 and YOLOX algorithms are significantly better than SSDs in terms of detection accuracy and speed, they are still inferior to the algorithm in this paper. In summary, this paper's algorithm is better than other models in detection speed and detection accuracy, which proves the feasibility of this paper's algorithm for crop pest target detection tasks. To justify the proposed method's complexity, comparisons were conducted with state-of-the-art classification models adapted to the multi-class classification task: ResNet-50, VGG-16, and EfficientNet-B3. These models were retrained on the 10,000-image dataset using transfer learning from ImageNet [21]. Key metrics are presented in Table 4.  
The combined model outperforms classical classifiers in accuracy and F1-score despite having higher computational complexity. This advantage is attributed to its ability to localize lesions, detect spatial distributions of diseases (which is critical for precision agriculture), handle small targets and identify sub-pixel lesions missed by classification models, and generalize across scales to maintain performance on both single-leaf and multi-plant images. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Params** &
\begin{tabular}{c} **Inference Time** \\ **(ms)** \\ \end{tabular} \\ \hline ResNet-50 & 89.23\% & 88.47\% & 25.6 M & 12.4 \\ VGG-16 & 85.18\% & 84.35\% & 138.4 M & 21.7 \\ EfficientNet-B3 & 91.56\% & 90.82\% & 12.3 M & 8.9 \\ \hline
**YOLOv7** & 94.73\% & 93.91\% & 37.8 M & 11.4 \\
**Combined Model** & 96.25\% & 95.84\% & 52.1 M & 13.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance comparison.