## 3 Results and Discussion
### Comparison and Ablation Results  
Following comprehensive comparative experiments, Table 4 showcases the outstanding performance of the WD-YOLO model among various contemporary object detection networks. Notably, WD-YOLO achieves an mAP of 65.4% at a 50% IoU and 53.1% at the more rigorous 50-95% IOU thresholds, outperforming the previous best YOLOv10l model, which registered 63.1% and 52.1% at these thresholds, respectively. This comparison underscores WD-YOLO's precision advantage and its adeptness in managing complex scenarios with significant object overlap, which is crucial for environments with complex or occluded scenes.  
Further analysis illustrates that WD-YOLO operates with merely 2.78 million parameters, a significant reduction of about 90% compared to the 25.76 million parameters of YOLOv10l. This drastic decrease not only reduces the model's storage and computational  
Figure 6: Training process comparison.  
demands but also boosts its suitability for deployment on edge devices, critical for real-time applications. Moreover, a smaller parameter count implies enhanced adaptability and learning efficiency, which accelerates the training process and reduces the likelihood of overfitting.  
Table 1 provides a detailed summary of the ablation experiments, demonstrating how the integration of the DSConv and WTConcat modules significantly boosted the mAP. Specifically, the fully integrated WD-YOLO model achieved an mAP of 65.4%, markedly higher than the baseline YOLOv10n model's 56.3%. This considerable improvement in mAP, achieved with only a minimal increase in the parameter count (consistently maintained at 2.78 M across configurations), affirms the effectiveness of the DSConv and WTConcat modules in enhancing detection accuracy without adding complexity to the network architecture.  
Moreover, the deployment of online distillation was also a crucial factor in enhancing model performance. For example, the models that incorporated distillation, such as WD-noDis, exhibited superior performance metrics compared to variants without distillation, such as WD-noDA, which achieved an mAP of 63.8%. This highlights the efficacy of online distillation in leveraging the capabilities of more complex models to enhance the performance of simpler, more efficient architectures.  
To further validate the model's performance, Figure 7 compares the prediction results from YOLOv10n, YOLOv10l, and WD-YOLO. WD-YOLO demonstrates greater accuracy and robustness, particularly in detecting small or overlapping disease areas, despite being significantly more lightweight than YOLOv10l.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **mAP50 (\%)** & **mAP50-95 (\%)** & **P (\%)** & **R (\%)** & **Params (M)** \\ \hline YOLOv5n & 56.6 & 48.2 & 59.3 & 55.4 & 2.84 \\ YOLOv8n & 58.0 & 49.6 & 58.2 & 56.7 & 3.34 \\ YOLOv10n & 58.8 & 48.0 & 59.7 & 56.9 & 2.71 \\ YOLOv10l & 63.1 & 52.1 & 60.5 & 60.1 & 25.76 \\ YOLOv-L & 58.8 & 44.4 & 58.7 & 55.6 & 4.06 \\ Faster-RCNN & 45.1 & 45.2 & 47.5 & 54.3 & 12.03 \\ SSD300 & 46.1 & 44.3 & 57.4 & 53.2 & 6.36 \\ RetinaNet & 52.2 & 46.6 & 54.6 & 52.3 & 9.97 \\ DETR & 48.7 & 45.7 & 53.4 & 51.2 & 12.23 \\ WD-YOLO & 65.4 & 53.1 & 60.4 & 60.1 & 2.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different algorithms for object detection.  
Figure 7: Labeled images and detection results.  
Additionally, Figure 8 presents a visual comparison between YOLOv10l and WD-YOLO on complex plant images. WD-YOLO produces clearer and more complete detections while using fewer parameters, showcasing its ability to maintain high detection capability under resource-constrained conditions.