## 4 Experiments and Analysis
### Ablation Studies  
To ascertain the efficacy of the improved network, we conducted ablation studies where we gradually integrated modules into the YOLOv8n network, maintaining consistent training parameters throughout the experimentation with the eggplant disease dataset. The data obtained from ablation studies are recorded in Table 3, where the symbol '\(\checkmark\)' is used to signify the inclusion of an addition, whereas '-' is used to convey that there has been no alteration.  
Experiment 1 demonstrates the performance of the original YOLOv8. In Experiment 2, we introduced the proposed C2f_SAConv module into the Backbone of YOLOv8. This module can adaptively switch between the outputs of convolutions with different dilation rates, thereby adjusting the receptive field. This facilitates the network's flexible learning of disease features of various shapes and sizes, resulting in an improvement in the [EMAIL_ADDRESS] and F1 score, though with a marginal increase in model size. Building upon Experiment 2, Experiment 3 employed the SlimNeck structure to reconstruct the feature fusion network. This network achieves model lightweighting by substantially decreasing the parameters  
Figure 13: Heat map comparison of YOLOv8n and YOLOv8-E (YOLOv8n in the middle, YOLOv8-E on the right).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & **C2f\_SAConv** & **SlimNeck** & **LSKA** & **Inner-MPDIoU** & **[EMAIL_ADDRESS] (\%)** & **F1 (\%)** & **GFLOPs** & **Params (MB)** \\ \hline
1 & - & - & - & - & 73.9 & 71.2 & 8.1 & 6.0 \\
2 & \(\checkmark\) & & - & - & 75.6 & 72.9 & 7.7 & 6.1 \\
3 & \(\checkmark\) & \(\checkmark\) & - & - & 75.8 & 72.9 & 6.9 & 5.6 \\
4 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & - & 76.5 & 73.5 & 7.0 & 5.6 \\
5 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 79.4 & 75.7 & 7.0 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The data from ablation experiments.  
and GFLOPs while maintaining high levels of accuracy. In Experiment 4, we embedded the LSKA module into SlimNeck, facilitating the model's attention to fine-grained information through cascaded depthwise convolution and depthwise dilated convolution. This effectively addresses the issue of missing small targets, ultimately enhancing the [EMAIL_ADDRESS] and F1 score.  
Experiment 5 integrated all improvements into YOLOv8, achieving the highest levels of 79.4% for [EMAIL_ADDRESS] and 75.7% for F1 score among all experiments. Compared to the original YOLOv8 model, YOLOv8-E showed a significant boost in detection accuracy, accompanied by a decrease in model complexity. Specifically, [EMAIL_ADDRESS] increased from 73.9% to 79.4%, the F1 score improved from 71.2% to 75.7%, the model size decreased by 0.4 MB, and computational complexity dropped by 1.1 GFLOPs. In essence, YOLOv8-E demonstrated remarkable efficiency across diverse evaluation criteria, substantially boosting detection precision while maintaining a lightweight model architecture.  
Additionally, Grade-CAM technology was employed to generate heat maps, serving as a visual representation of the detection effectiveness of various optimized models, as detailed in Figure 14. In these heat maps, an increase in color intensity signifies a higher level of attention paid by the model to the relevant areas. In Figure 14, the heat maps produced by models incorporating optimization strategies exhibit a more pronounced focus on the target regions. Notably, the YOLOv8-E model, incorporating all optimization methods, has maximized its focus on disease regions. This further substantiates the enhanced overall detection performance of the optimized models.