## 5 Conclusions  
In this study, we successfully constructed and validated a crop pest and disease recognition framework based on an improved YOLOv7-U-Net combined network. By introducing targeted architectural enhancements, the proposed model effectively addresses the precision limitations of existing crop image recognition methods. Specifically, the YOLOv7 network was optimized with a self-attention mechanism in the SPPCSPC module to suppress redundant information and enhance channel-wise feature weighting, while the PAFPN structure was upgraded to improve cross-scale feature fusion for small object detection. For U-Net, the integration of the CBAM attention module and depth-separable convolutions strengthened spatial-channel feature interaction and reduced computational complexity, enabling more accurate boundary segmentation.  
Experimental results demonstrated significant performance improvements: the combined model achieved 97.49% detection accuracy, a 96.91% mean average precision (mAP), and a detection speed of 90.41 FPS, outperforming mainstream algorithms such as YOLOv5 and Faster R-CNN in key metrics. The improved U-Net also exhibited robust convergence, with its loss function decreasing to near 0 during training, validating the effectiveness of the proposed modifications. Despite these advancements, the model faces notable technical challenges. Its generalization capability remains limited, achieving only 92.3% accuracy on unseen disease classes and suffering performance degradation under adverse field conditions--6.8% accuracy drop under low light (500 lux) and 8.2% under rain-like noise. Small-lesion detection (lesions < 2 mm\({}^{2}\)) remains problematic, with a 21% failure rate in identifying early-stage symptoms, which hinders its application in pre-symptomatic disease monitoring [22].  
To address these limitations, future research will focus on multiple directions. First, curriculum learning combined with synthetic weather augmentation will be employed to improve domain generalization under complex field conditions. Second, integrating transformer-based global context modules into the network architecture aims to enhance small-lesion detection by capturing long-range feature dependencies. Additionally, self-supervised pre-training on large-scale noisy agricultural datasets will be explored to boost noise resilience. For practical deployment, model quantization and pruning techniques will be applied to reduce inference latency by 40% for real-time edge device applications while maintaining acceptable accuracy levels. These enhancements collectively aim to elevate the model's robustness, efficiency, and applicability in real-world agricultural scenarios, ensuring its utility for timely pest management and crop health monitoring.  
Conceptualization, W.X.; Methodology, W.X.; Software, X.L.; Validation, W.X.; Formal analysis, X.L.; Data curation, X.L.; Writing--review & editing, W.X. and Y.Y.; Visualization, Z.L.; Supervision, Z.D.; Project administration, Z.D. and Y.Y.; Funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.