## 4 Discussion  
Compared to prior studies and conventional approaches, the proposed YOLOv8n-WSE-pest model demonstrates several significant advantages over traditional models. By incorporating WIoU-v3 into the loss function, the model effectively addresses the limitations of the original IoU metric in describing boundary differences and detecting small objects. This improvement allows for a more precise description of the boundary differences between predicted and ground truth boxes, thereby enhancing the detection performance. Additionally, the introduction of SCConv helps in reducing feature redundancy, which, in turn, lowers the model's complexity while maintaining high precision. The Efficient Multi-Scale Attention (EMA) module further enhances the model's ability to recognize small, multiple, and interfered targets by utilizing multi-scale pixel-level attention with global contexts. To conclude, these enhancements delivered outstanding performances during external validation. Notably, the precision improved by 3.12% compared to the original network while also reducing the parameter count by 25,348, thereby highlighting the model's potential for deployment and application.  
However, despite these advancements, the YOLOv8-WSE-pest model still faces challenges, particularly with complex image datasets. Variations in environmental conditions, such as the lighting and weather, can impact the model's detection accuracy. Additionally, the presence of interfering factors, such as vegetation and unrelated pests, complicates the detection process. The SCConv module, while effective in reducing feature redundancy,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & **AP of _Toxoptera_** & **AP of _Xyleborns_** & **AP of _Empoasca_** & **AP of _Matthodes_** & \\  & _aurnatti_**\%** & _fornicatus_ & _pirisuga_ & _disicicollis [PERSON]_ & **mAP/\%** \\  & & _[PERSON]_**\%** & _[PERSON]_**\%** & _[PERSON]_\%** & \\ \hline Faster-RCNN & 84.62 & 83.78 & 82.76 & 83.25 & 83.61 \\ SSD & 89.84 & 88.23 & 88.74 & 89.57 & 89.10 \\ YOLOv8n & 94.07 & 95.53 & 96.12 & 97.36 & 95.77 \\ YOLOv8n-WSE-pest & 97.25 & 98.11 & 97.82 & 98.62 & 97.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing the _AP_ values of various insect species in the model.  
Figure 15: AP recognized using different models in different categories.  
does not entirely eliminate these challenges. Similarly, the EMA module, though it improves the recognition capability, may still struggle with scenes of high complexity and clutter. These limitations highlight the need for further refinement and optimization of the model. To optimize these limitations, future research will focus on collecting more data on environmental variations and pest species, which will aid the model in generalizing better under diverse conditions. Additionally, further optimizing the model's structure and exploring the practical applications of deep learning models in agricultural science and technology, while adding functionalities such as pest counting and tracking, will be essential for the model's practical deployment.  
In summary, the YOLOv8n-WSE-pest model significantly improves detection performance through WIoU-v3, SCConv, and EMA. Subsequent studies should concentrate on addressing the current limitations through the exploration of advanced data augmentation techniques and optimizing the model for real-time applications in diverse environments. These efforts will contribute to providing more robust and practical solutions for the intelligent and automated management of agricultural pest control.