## 1 Introduction  
Plant disease detection in real-world agricultural settings faces three persistent challenges: (1) the wide variability in symptoms across different growth stages and disease types, (2) complex environmental factors such as occlusions from leaves, varying lighting conditions, and background clutter, and (3) poor generalization of models when applied across different crop species or geographic regions. These factors significantly hinder the scalability and robustness of traditional detection systems. To overcome these challenges, recent research has shifted toward more adaptable and efficient detection strategies. Multiscale feature extraction has been widely adopted to capture symptoms of varying sizes and patterns, while few-shot and data-efficient learning approaches have been explored to address the scarcity of labeled samples for rare or emerging diseases. At the same time, real-time performance remains critical for field deployment, pushing the community toward single-stage detectors that balance speed and accuracy.  
Concurrently, the YOLO series [1] has consistently redefined benchmarks for detection speed and accuracy. Its latest iterations incorporate advanced mechanisms such as cross-stage partial networks and path aggregation, facilitating improved feature integration across scales and transitioning from multi-stage to single-stage detection processes, thereby enabling real-time performance.  
Knowledge distillation [2] has also gained recognition as a potent method for transferring and condensing knowledge from larger, complex models to smaller, more efficient ones. This technique is especially valuable in deploying high-performing models on devices with limited resources typical in agricultural settings, thus enabling real-time, in-field disease detection. Recent models, such as the YOLOX-ASSANano proposed by [PERSON] et al. [3], are tailored specifically for real-time detection of apple leaf diseases. This model, with its innovative backbone structure, enhances feature extraction but is primarily designed for apple diseases, which may limit its general applicability. Likewise, research by [PERSON] et al. [4] employing the EfficientNetV2 architecture demonstrates the integration of lightweight models suited for field deployment, albeit potentially at the expense of detection range and accuracy due to model simplifications.  
Despite progress, significant gaps remain. Existing solutions often overfit to specific imaging conditions (e.g., controlled lighting) or require species-specific tuning. For instance, YOLOX-ASSANano is tailored for apple leaf diseases, limiting generalization. Similarly, [PERSON] et al. adopted EfficientNetV2 with reduced detection scope. Comprehensive evaluations by [PERSON] [5] reveal that field-deployed models typically experience 15-30% accuracy drops compared to laboratory validation. Thus, the need persists for models balancing applicability, accuracy, and real-time efficiency.  
To address these gaps, we propose WD-YOLO, featuring:  
* **DSConv Module:** Dual-scale convolution for symptom variability adaptation.
* **WTConcat Module:** Weighted fusion for complex field condition robustness.
* **Online Knowledge Distillation:** Cross-species knowledge transfer.  
WD-YOLO achieves state-of-the-art performance on multiple crop datasets while maintaining real-time efficiency, advancing sustainable crop management.