## 3 Experiments and Analysis
### Improvement Experiments  
#### 3.5.1 Ablation Experiments  
In this paper, eight sets of experiments were designed to verify the impact of the three aspects of GFCD-YOLOXS improvement and their combinations on the results of grape disease identification, as shown in Table 1, where \"\(\surd\)\" indicates that the module was added in this experiment.  
As can be seen from Table 1, the addition of the FOCUS module increased the mAP values for all disease identification methods by 1.15% compared to the original YOLOXS, as the use of the FOCUS network to connect features of different depths while retaining the complete features reduced the lack of information on features related to grape diseases; the introduction of the CBAM module also increased the mAP values for all disease identification methods compared to the original YOLOXS. The introduction of the CBAM module increased the mAP values for all disease recognition methods by 0.41%, as the introduction of the CBAM module allowed the network to focus on key features of grape diseases; the introduction of only double residual edges decreased the mAP values for all disease recognition by 0.25%, because the introduction of only residual edges caused the model to generate more noise, resulting in a decrease in the recognition rate when the CBAM module and DRES were introduced, compared to the introduction of only the CBAM module. This result occurred because the introduction of these two modules allows the model to focus on key features while connecting non-key features for recognition; when the three improved modules are used together, the highest mAP value of 99.10% is achieved for all grape disease recognition methods, which is because the improvement not only preserves the complete information but also allows the model to connect non-key features while focusing on key features. In addition, the improved ModelSize did not increase excessively, and the FPS values did not decrease.  
To further demonstrate the effectiveness of these three improvements, we plotted the loss function and mAP plots for the validation set during formal training of the ablation experiment. The loss function plot is shown in Figure 13, with the loss values decreasing and leveling off as the training Epoch increases, and the GFCD-YOLOXS model having the lowest loss values. The mAP plot is shown in Figure 14. The mAP values gradually increased and leveled off with increasing training Epoch, with the highest mAP values for GFCD-YOLOXS. Combined with the analysis in Figures 13 and 14, the GFCD-YOLOXS model improved in this paper has the most effective recognition effect.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **FOCUS** & **CBAM** & **DRES** & **mAP** & **ModelSize (M)** & **FPS** \\ \hline YOLOXS & & & & 97.05\% & 34.3 & 64.58 \\ YOLOXS\_1 & \(\surd\) & & & 98.20\% & 37.0 & 50.21 \\ YOLOXS\_2 & & \(\surd\) & & 97.46\% & 34.4 & 53.57 \\ YOLOXS\_3 & & & \(\surd\) & 96.80\% & 34.3 & 64.32 \\ YOLOXS\_4 & \(\surd\) & \(\surd\) & & 98.71\% & 37.1 & 49.83 \\ YOLOXS\_5 & \(\surd\) & & \(\surd\) & 98.22\% & 37.0 & 50.14 \\ YOLOXS\_6 & & \(\surd\) & \(\surd\) & 97.64\% & 34.4 & 53.32 \\ GFCD- & \(\surd\) & \(\surd\) & \(\surd\) & 99.10\% & 37.1 & 48.57 \\ YOLOXS & \(\surd\) & \(\surd\) & \(\surd\) & 99.10\% & 37.1 & 48.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of mAP in ablation experiment.