## 3 Experiment and Analysis
### Experimental Results and Analysis
#### 3.2.5 Performance Comparison of Different Models  
In order to make the experimental results more reliable and convincing and explore the detection performance of different models against rice diseases and pests, Faster R-CNN, yolov1, yolov4, and yolov5 were used for comparison with the YOLOV5-RSAN algorithm proposed in this paper under the same experimental environment conditions [29; 30; 31; 32]. Table 4 lists the performance comparison of the test results.  
As can be seen from Table 4, compared with other models, the accuracy rate of the YOLOV5-RSAN algorithm proposed in this paper is 1.7, 5.4, 4.6, and 2.7 percentage points higher than that of Faster R-CNN, yolov1, yolov4, and yolov5, respectively, and it is better than other models in terms of mAP and fps. The mAP of the yolov5-RSAN algorithm is as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Model** & **Accuracy Rate (\%)** & **mAP (\%)** & **fps (f/ms)** \\ \hline Faster R-CNN & 93.1 & 89.2 & 8.9 \\ yolov1 & 89.4 & 89.5 & 10.3 \\ yolov4 & 90.2 & 90.5 & 10.5 \\ yolov5 & 92.1 & 90.1 & 11.0 \\ yolov5-RSAN & 94.8 & 94.4 & 13.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance of each algorithm model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**RepVGG Network Structure** & **SK Attention Mechanism** & **Adaptive NMS** & **Accuracy Rate (\%)** & **Recall Rate (\%)** & **mAP (\%)** & **AP (\%)** & **fps (f/ms)** \\ \hline y & n & n & 92.3 & 89.5 & 91.2 & 90.3 & 11.5 \\ y & y & n & 92.6 & 89.9 & 91.5 & 91.3 & 11.9 \\ y & y & y & 94.8 & 93.1 & 94.4 & 96.7 & 13.8 \\ y & n & y & 93.4 & 92.8 & 92.3 & 92.6 & 12.3 \\ n & y & n & 92.9 & 92.1 & 92.1 & 91.5 & 11.6 \\ n & n & y & 92.6 & 91.6 & 91.8 & 90.9 & 11.8 \\ n & y & y & 93.1 & 91.2 & 93.5 & 94.5 & 13.1 \\ n & n & n & 92.1 & 89.2 & 90.1 & 89.3 & 11.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation results.  
Figure 7: Performance counters of the yolov5-RSAN algorithm.  
high as 94.4%, which is more suitable for the detection of rice diseases and pests, and lets it effectively detect diseases and pests so as to timely carry out the prevention and control of rice diseases and pests and reduce the impact of diseases and pests on rice yield to a certain extent. For the detection of several major rice pests and diseases proposed in this paper, the detection effect diagram of the improved algorithm is shown in Figure 8.  
The most important and effective way to improve the accuracy of yolov5 target detection is to improve the degree of feature extraction. In this paper, the SK attention mechanism is introduced at the back of the Neck module to integrate the feature information collected by each CSP2_X structure, which greatly improves the efficiency of feature extraction. This improves accuracy. The accuracy rate was 94.8%. Experimental results show that the accuracy of the improved algorithm is higher than that of the other object detection algorithms mentioned in this paper. A RepVGG network structure is added to the Backbone. conv+BN layers are reused, layers are merged, and the number of layers is reduced to improve the training speed and the speed of the model. According to the ablation experiment results, the fps of model detection is increased by about 0.5 f/ms when only the RepVGG network structure is added. In theory, this shows that the model can detect an additional 0.5 frames in the same time unit, which also indicates that the detection speed of the model is improved. According to the experimental results and the above analysis, the performance of the improved yolov5-RSAN algorithm has been improved, especially in the aspects of accuracy, speed, mAP, etc., and some improvements have also been made in improving the problems of small target detection, such as missed detection and false detection.