## 3 Experiments and Analysis
### Comparison of the Model in This Paper with Other Models  
#### 3.6.1 About This Dataset Compared to Other Models  
In order to verify the feasibility of the GFCD-YOLOXS algorithm, this paper compares the model with the existing classical target detection models Faster-RCNN [33], SSD [34], RetinaNet [35], YOLOVSS, and Eff-B3-YOLOV3 [19]; the experimental results are shown  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Disease Name** & **SE** & **CA** & **CBAM** \\ \hline Sour rot & 95.16\% & 95.86\% & 95.26\% \\ Anthracnose & 99.91\% & 96.93\% & 99.90\% \\ Black rot & 100.00\% & 100.00\% & 100.00\% \\ Black spot & 100.00\% & 100.00\% & 100.00\% \\ Botrytis cinerea & 100.00\% & 100.00\% & 100.00\% \\ Brown spot & 100.00\% & 100.00\% & 100.00\% \\ Deficiency & 95.78\% & 97.47\% & 96.48\% \\ Downy mildew & 98.77\% & 99.85\% & 99.64\% \\ Esca & 100.00\% & 100.00\% & 100.00\% \\ Felt diseases & 98.40\% & 97.41\% & 98.21\% \\ Healthy grape & 99.28\% & 99.83\% & 99.36\% \\ Healthy leaves & 99.20\% & 99.06\% & 99.69\% \\ Leaf roll & 99.18\% & 99.04\% & 98.11\% \\ Phylloxera & 97.06\% & 99.86\% & 99.90\% \\ Powdery mildew & 99.97\% & 99.98\% & 99.99\% \\ All diseases & 98.85\% & 99.02\% & 99.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different attention mechanism modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Disease Name** & **No RES** & **RES** & **DRES** \\ \hline Sour rot & 93.99\% & 93.66\% & 95.26\% \\ Anthracnose & 99.73\% & 98.42\% & 99.90\% \\ Black rot & 100.00\% & 100.00\% & 100.00\% \\ Black spot & 100.00\% & 100.00\% & 100.00\% \\ Botrytis cinerea & 100.00\% & 100.00\% & 100.00\% \\ Brown spot & 100.00\% & 99.98\% & 100.00\% \\ Deficiency & 96.64\% & 96.42\% & 96.48\% \\ Downy mildew & 99.73\% & 99.88\% & 99.64\% \\ Esca & 100.00\% & 99.95\% & 100.00\% \\ Felt diseases & 98.24\% & 98.58\% & 98.21\% \\ Healthy grape & 99.48\% & 98.63\% & 99.36\% \\ Healthy leaves & 99.07\% & 99.36\% & 99.69\% \\ Leaf roll & 95.14\% & 99.18\% & 98.11\% \\ Phylloxera & 98.71\% & 99.34\% & 99.90\% \\ Powdery mildew & 99.98\% & 100.00\% & 99.99\% \\ All diseases & 98.71\% & 98.89\% & 99.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different residual edges.  
in Table 4. Table 4 shows that the GFCD-YOLOXS recognition method proposed in this paper has the highest accuracy of 99.10%. Moreover, its model size and detection speed are smaller and faster than Faster RCNN, SSD, RetinaNet, and the previously studied Eff-B3-YOLOV3 model, meaning that it can meet the requirements of real-time detection of plant protection equipment.  
Furthermore, three pictures suffering from grape powdery mildew, grape leaf roll, and grape deficiency were selected to compare the detection effects of different models. The experimental results are shown in Figure 15. When using the Faster RCNN model, grape powdery mildew appears to be incorrectly identified, and grape leaf roll and grape deficiency appear to be missed in complex backgrounds; when using the SSD model, grape powdery mildew appears to be missed in boundary identification, grape leafroll appears to be missed in dim environment identification, and grape deficiency appears to be missed in the presence of occlusion; and when using the RetinaNet model, grape powdery mildew appears to be missed. The detection of small targets in grapevine leaf roll and grapevine deficiency improved when using the RetinaNet model; however, there were still missed detections. When using the YOLOV5S model, grapevine deficiency was missed for complex backgrounds and small targets. In Eff-B3-YOLOV3, the overall confidence of the identification results was low; in contrast, In contrast, the use of GFCD-YOLOXS reduces feature loss due to the inclusion of the FOCUS network to fuse features of different depths, thus reducing missed detection, and the inclusion of CBAM, double residual edges, allows the network to focus on key features while linking non-key features, thus improving the accuracy of model detection.