## 3 Results
### Experimental Analysis of Detection Model  
#### 3.2.1 Ablation Experiment  
To further validate the accuracy of the proposed model in this study, several ablation experiments were conducted using different models, including YOLOv8n, YOLOv8n-W, YOLOv8n-S, YOLOv8n-E, YOLOv8n-WS, YOLOv8n-WE, YOLOv8n-SE, and YOLOv8n-WSE-pest. As shown in Table 2, YOLOv8n-W is a model that improves the original YOLOv8n by incorporating the WIoU-v3 loss function. YOLOv8n-S incorporates the SCConv module into the Backbone network of YOLOv8n to optimize the spatial and channel feature extraction. YOLOv8n-E introduces the EMA module into the Neck network of YOLOv8n to enhance model attention. As shown in Table 3, compared to the original network, the introduction of the WIoU-v3 loss function in YOLOv8n-W increased the precision by 0.40%, recall by 2.49%, and mAP50 by 0.79%. The addition of the SCConv module improved the precision by 1.02%, recall by 2.77%, and mAP50 by 1.01%. The inclusion of the EMA attention mechanism increased the precision by 1.69%, recall by 6.63%, and mAP50 by 2.11%. This module had the most significant impact on precision improvement but also increased the parameters, gradients, and GFLOPs by 8,011,984, 8,011,984, and 20.2, respectively. When comparing YOLOv8n-SE with YOLOv8n-E, the former showed similar precision, recall, and mAP50 but significantly reduced parameters, gradients, and GFLOPs by 8,037,332, 8,037,332, and 20.7, respectively. EMA enhanced the model's localization and prediction performance, but it also increased the model's parameter count and complexity. On top of that, incorporating the SCConv module significantly reduced the parameter count during feature extraction while maintaining model accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **WIoU-v3** & **SCConv** & **EMA** & **Characteristics** \\ \hline YOLOv8n & \(\times\) & \(\times\) & \(\times\) & Baseline \\ YOLOv8n-W & \(\surd\) & \(\times\) & \(\times\) & Precision focused \\ YOLOv8n-S & \(\times\) & \(\surd\) & \(\times\) & Efficiency \\ YOLOv8n-E & \(\times\) & \(\times\) & \(\surd\) & High performance \\ YOLOv8n-WS & \(\surd\) & \(\surd\) & \(\times\) & Balanced \\ YOLOv8n-WE & \(\surd\) & \(\times\) & \(\surd\) & Enhanced \\ YOLOv8n-SE & \(\times\) & \(\surd\) & \(\surd\) & Simplified \\ YOLOv8n-WSE-pest & \(\surd\) & \(\surd\) & \(\surd\) & Optimized \\ \hline \hline \end{tabular} Note: The table presents the characteristics of various YOLOv8n algorithm versions, indicating the presence (\(\surd\)) or absence (\(\times\)) of specific features. WIoU-v3 is a precision-focused module, SCConv is designed for efficiency, and EMA is associated with high performance. The “Baseline” version, YOLOv8n, lacks these modules. The suffixes “-W”, “-S”, “-E”, “-WS”, “-WE”, “-SE”, and “-WSE-pest” denote different combinations of these modules, with each version aiming to achieve a particular balance or optimization in performance. For instance, YOLOv8n-WSE-pest combines all three modules for the most optimized version.  
\end{table} 
Table 2: An overview of the performance characteristics of our different YOLOv8n versions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Precision/\%** & **Recall/\%** & **mAP50/\%** & **Layers** & **Parameters** & **Gradients** & **GFLOPs** \\ \hline YOLOv8n & 91.96 & 88.54 & 95.77 & 225 & 3,157,200 & 3,157,184 & 8.9 \\ YOLOv8n-W & 92.36 & 91.03 & 96.56 & 225 & 3,157,200 & 3,157,184 & 8.9 \\ YOLOv8n-S & 92.98 & 91.31 & 96.78 & 236 & 3,131,180 & 3,131,164 & 8.3 \\ YOLOv8n-E & 93.65 & 95.17 & 97.88 & 233 & 11,169,184 & 11,169,168 & 29.1 \\ YOLOv8n-WS & 92.67 & 91.93 & 96.47 & 236 & 3,131,180 & 3,131,164 & 8.3 \\ YOLOv8n-WE & 94.14 & 96.41 & 97.16 & 233 & 11,169,184 & 11,169,168 & 29.1 \\ YOLOv8n-SE & 94.29 & 95.75 & 97.42 & 244 & 3,131,852 & 3,131,836 & 8.4 \\ YOLOv8n-WSE-pest & 95.08 & 94.19 & 97.95 & 244 & 3,131,852 & 3,131,836 & 8.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiment.