# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 4 Discussion  
A series of experiments conclusively demonstrated the superiority of the proposed EMCF model in soybean weed detection. Through ablation studies, we validated the advantages of each model module. In the backbone network design, we integrated multi-channel mechanisms with FasterNet [40], effectively leveraging its convolutional layers to adaptively select or exclude specific pixels, thereby reducing computational complexity and parameter count. Simultaneously, the gating signals from the multi-channel mechanism enhanced feature extraction capabilities. Within the AIFI framework, we replaced traditional multi-head attention mechanisms with EA attention mechanisms [41]. This approach not only facilitates upper-layer feature extraction but also intrinsically strengthens feature representation through its inherent mechanism, significantly improving local feature extraction performance. Moreover, we innovatively constructed the EMFF by combining diffusion network structures with a fusion module, enabling the model to more comprehensively capture local features and global information, thereby enhancing both accuracy and processing speed. This strategy resonates with the multiscale receptive field optimization techniques in [54], which emphasized the importance of parallelized multiscale operations for efficient feature integration in cluttered environments. Notably, the fusion module processes multiple depth-wise separable convolutions in parallel across different scales, progressively expanding the receptive field and performing more nuanced feature fusion across channels. This approach provides richer information for subsequent network layers, further optimizing the model's detection capabilities. We compared YOLOv10m [52], YOLOv9m [46], YOLOv6m [36], YOLOv5m [35], YOLOv3t [51] and the EfficientNet [50] single-stage real-time monitoring models. Compared with YOLOv5m [35] and YOLOv9m [46], our model has a slight compromise in mAP50:95. The main reason is that it is based on lightweight design, and its main advantage is to reduce redundant calculations. For example, Faster-Block in CGF-Block contains a large number of \(1\times 1\) convolutions, which reduces the number of parameters but may weaken the ability to model spatial features. However, from the overall comparative experimental results, it can be seen that, while maintaining competitive detection accuracy, the detection speed is also maintained at the same level, or even slightly improved. For the two-stage detection models Faster R-CNN [23], DINO [49], RT-DETR [34], and Deformable-DETR [37], our model has significantly improved the detection accuracy and detection speed while significantly reducing the number of parameters and calculations. Finally, to verify the generalization ability of the model, we conducted comparative experiments with the baseline model on the aerial weeds dataset. The results show that our model can still achieve significant improvements in accuracy and real-time detection compared to the baseline model, despite some differences from the dadao_cc dataset. Through the visualization of the detection results, there are fewer missed detections and false positives compared to the baseline, especially in areas where crops and weeds co-exist. This is consistent with the quantitative gains in recall and mAP, ensuring real-time applicability in resource-limited agricultural deployments. Although our model shows good generalization ability, it is still difficult to say that the model has perfect practicality in different application scenarios. Therefore, we plan to explore the integration with automated phenotyping [55], spectral fusion [56], and optical measurement accuracy [57; 58] technologies in the agricultural field to prepare for the deployment of future models in real-world environments. Compared to the weed detection model proposed in [59; 60; 61], the EMCF-RTDETR model proposed in this paper has a leading position in terms of average accuracy and detection speed, and the number of parameters is greatly reduced.