## 3 Results and Discussion
### Discussion  
To contextualize our results within the same PlantDoc benchmark, we compare WD-YOLO with key existing methods in Table 6. The M_YOLOv4 achieves 55.45% mAP50 and 56.0% recall, while the improved YOLOv5 reaches 58.2% mAP50 and 55.0% recall. Our WD-YOLO significantly outperforms these with **65.4% mAP50** and **60.1% recall**--a **12.4% mAP50 improvement** over the state-of-the-art YOLO variant. This demonstrates the efficacy of our DSConv and WTConcat modules in capturing nuanced disease patterns. The WD-YOLO model incorporates the innovative DSConv and WTConcat modules to tackle the multifaceted challenges of plant disease detection efficiently. These modules enhance feature perception and facilitate dynamic integration, enabling the model to adapt to the diverse manifestations of plant diseases. With a demonstrated mAP50 of 65.4%, WD-YOLO surpasses traditional methods in both accuracy and computational efficiency. In comparison, the transformer-based method by [PERSON] et al. [23], which achieves a precision of 70.3%, utilizes attention mechanisms and equalization loss to address the challenges posed by imbalanced datasets. Although this method shows high precision, it lacks specific optimizations for computational constraints typical in field deployments. Conversely, our WD-YOLO model ensures that superior accuracy is achieved without compromising efficiency, making it exceptionally suitable for resource-limited agricultural environments. The study by [PERSON] et al. [4] with the EfficientNetV2 architecture reports an accuracy of up to 74% in controlled test environments. However, EfficientNetV2's static scaling does not offer the responsive adaptability that our DSConv module provides, which dynamically adjusts to variations in scale and symptom presentation of plant diseases, thereby ensuring more precise detections under varied field conditions. [PERSON] et al. 's [24] application of the YOLOv8 architecture, while offering detailed segmentation and robust accuracy, demands considerable computational resources. This high computational requirement makes YOLOv8 less viable for in-field agricultural applications where processing power is often limited. Our WD-YOLO model, however, maintains comparable accuracy but with much lower computational demands, supporting real-time detection applications more effectively. [PERSON] et al. [25] combine Inception modules with vision transformers to achieve an exceptional accuracy of 99.94% under optimal conditions. Despite its high accuracy, the complexity and size of their model limit its deployment in practical agricultural settings, where quick response and minimal computational load are crucial. In contrast, the WD-YOLO model offers a streamlined yet powerful architecture that is more feasible for real-world applications, ensuring robust performance without extensive hardware requirements. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **mAP50 (\%)** & **Recall (\%)** & **Params (M)** \\ \hline M\_YOLOv4 [21] & 55.45 & 56.0 & - \\ Improved YOLOv5 [22] & 58.2 & 55.0 & **2.20** \\
**WD-YOLO (Ours)** & **65.4** & **60.1** & 2.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis on PlantDoc dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Noise Type** & **mAP50 (\%)** & **P (\%)** & **R (\%)** \\ \hline No noise & 65.4 & 60.4 & 60.1 \\ Gaussian noise (1) & 64.6 & 59.8 & 59.2 \\ Gaussian noise (5) & 62.9 & 58.2 & 57.4 \\ Salt-and-pepper (0.01) & 65.0 & 58.6 & 59.2 \\ Salt-and-pepper (0.05) & 63.4 & 55.9 & 55.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of WD-YOLO across different noise conditions. [PERSON] et al. 's [26] investigation into few-shot learning demonstrates the potential of achieving high accuracy with minimal training data. While effective in data-sparse scenarios, this approach might not perform well with the variability and complexity found in larger, more diverse datasets.