# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 5 Analysis of Experimental Results  
### Ablation Experiment  
To verify the application effect of the improvement strategies mentioned earlier, this paper sets four groups of experiments based on the principle of ablation to analyze the role of different modules on model improvement. This experiment is conducted on two datasets of rice and cotton, and all experimental groups have the same environmental parameters and model parameters. The experimental results are shown in Table 2 below, indicating that the improved model has different enhancement effects in both detection accuracy and detection speed. The ablation experiments can demonstrate the performance of the proposed model in this paper under different improvement states. In Version 1, the GAM module is enabled, and inserted after the SPPF module in the Backbone network. Its main role is to weigh important feature information, thereby improving detection accuracy. The experimental results demonstrate that the model in Version 1 improves the detection accuracy on both datasets, with better performance on the cotton dataset with complex disease and pest categories. However, there is a loss in detection speed. Version 2 enables the RFA Conv module, that is, in the mixed feature part of the Neck, the RFA Conv method replaces standard convolution operations. Its main function lies in the reduction of calculation costs, thereby increasing the calculation speed. The experimental results show that the model in Version 2 improves the calculation speed on both datasets. Version 3, building on Version 2, enables the GAM to improve the accuracy of calcul \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**GAM**} & \multirow{2}{*}{**RFA Conv**} & \multicolumn{3}{c}{**Rice**} & \multicolumn{3}{c}{**Cotton**} \\ \cline{3-8}  & & & **mAP** & **F1** & **FPS** & **mAP** & **F1** & **FPS** \\ \hline YOLOv8 & \(\times\) & \(\times\) & 66.47\% & 0.47 & 57.18 & 79.56\% & 0.71 & 57.82 \\ \hline Version 1 & \(\surd\) & \(\times\) & 67.66\% & 0.51 & 56.45 & 80.91\% & 0.75 & 56.76 \\ \hline Version 2 & \(\times\) & \(\surd\) & 68.04\% & 0.48 & 56.10 & 80.01\% & 0.72 & 56.32 \\ \hline Version 3 & \(\surd\) & \(\surd\) & 71.27\% & 0.53 & 55.57 & 82.91\% & 0.77 & 55.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the ablation experiment. The “\(\times\)” means that the module is not added. The “\(\surd\)” means that the module is added in the model. the calculation speed. According to experimental data, the above-mentioned improved modules have different degrees of improvement in the accuracy and speed of the model. The improved model mAP values reach 65.97% in the rice dataset and 82.91% in the cotton dataset, representing an average improvement of 3.3% compared to the original model. The average F1 score reaches 0.53 in the rice dataset and reaches 0.77 in the cotton dataset, having an average increase of 0.055. The improvements in the above indicators show that the proposed model in this paper has strong reference significance for improving the accuracy of recognition models of plant pests and diseases. To assess the reliability of the test results, this paper set confusion matrices to analyze the proposed improved YOLOv8 model and the original YOLOv8 model. The results of the confusion matrices are shown in Figure 9.  
The confusion matrix consists of row vectors representing the predicted class and column vectors representing the true class. Upon examining the confusion matrix of the proposed improved YOLOv8 model, it is evident that the recall rate experienced a significant increase compared to that of the original YOLOv8 model. Specifically, in the rice dataset, the average recall rate saw an improvement of 8.51%. However, there are a few classification errors in the blast and brown spot categories, possibly due to the misjudgments due to the similarity in shape and color of these two diseases.