## 3 Results  
### Comparative Experiments of Different Backbone Networks  
This study included a series of comparative experiments aimed at comprehensively examining the profound impact of diverse backbone networks on the performance of the YOLOv11 model in the detection tasks related to maize leaf diseases. The experiments evaluated five mainstream lightweight backbone network models, including CFNet, FasterNet, GhostNetV2, MobileViTBv3, and RepLKNet, where each was integrated into the YOLOv11 model architecture. These models were then evaluated against YOLOv11's native backbone network, C2K3. Table 2 reports the corresponding experimental results.  
Table 2 highlights significant performance variations in the maize disease detection tasks depending on the backbone network. For instance, the YOLOv11 model with RepLKNet as the backbone (YOLOv11s-RepLKNet) demonstrated notable comprehensive advantages in four key aspects: detection accuracy, detection speed, model complexity, and resource usage, particularly excelling in detection accuracy. RepLKNet achieved the highest values in both precision and F1 score, reaching 89.1% and 84.2%, respectively. This indicates that RepLKNet has a significant advantage in terms of detection accuracy, effectively reducing false positives and missed detections. Additionally, its recall reached 79.9%, demonstrating its considerable detection completeness and robust performance. Although its [EMAIL_ADDRESS]-0.95 was slightly lower than some of the models, its average precision at different confidence thresholds ([EMAIL_ADDRESS]) remained high (87.1%), proving its detection capability under multi-threshold conditions.  
The detection speed of YOLOv11s-RepLKNet was 3.2 ms per image, comparable to YOLOv11s-CFNet and significantly better than YOLOv11s-MobileViTBv3 (4.4 ms per image). This demonstrates that RepLKNet can achieve fast detection speeds while maintaining high accuracy, rendering it appropriate for real-time detection of maize diseases in practical application scenarios.  
From the perspective of model complexity and resource usage, although YOLOv11s-RepLKNet has a slightly higher parameter count and computational load compared with some lightweight models, the significant improvement in its detection accuracy justifies its complexity with reasonable cost-effectiveness. In contrast, other models like YOLOv11s-C2K3, while faster in detection speed, have slightly inferior detection accuracy. Through its unique architectural design, RepLKNet balances lightweight design and high performance, enabling it to excel in complex disease detection tasks. The memory footprint of YOLOv11s-RepLKNet is 20.2 MB; although slightly higher than some lightweight models, this is still within a reasonable range. Considering its advantages in detection accuracy and speed, this level of resource usage is acceptable and can meet the demands of most practical application scenarios.  
In summary, the YOLOv11 model with RepLKNet as the backbone performs exceptionally well in maize disease detection tasks, particularly in detection accuracy. Its outstanding performance in key metrics such as precision and F1 score, combined with a reasonable balance between detection speed and resource usage, makes it a highly competitive choice for these tasks. These results fully demonstrate RepLKNet's excellent trade-off between lightweight design and high-performance detection, delivering a reliable and efficient solution for the detection of maize diseases.