# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 5 Conclusions  
Considering that there are many weed species in soybean fields, it is difficult to accurately recognize weeds and soybean crops by conventional detection methods; for this reason, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR [34]. The experimental results show that the mAP50, mAP50:95 and FPS increased by 3.2%, 2.2% and 10%, respectively, with a reduction in the number of parameters and computation over the benchmark model. Compared to the YOLO series of real-time detection models, the detection speed remains at the same level while maintaining the leading detection accuracy. Especially comparing to the newly released YOLOv10m [52], there is an increase by 3.1% vs. 0.7% in mAP50 and mAP50:95, respectively. Finally, in order to verify the generalization ability of the present model, we compared it with the benchmark model on the Aerial Weeds dataset, and the results show that the accuracy is still leading and reduces the possibility of misdetection and omission.  
Despite the model's improvement in detection accuracy, its real-time detection speed has not yet been significantly improved for mainstream object detection models. Therefore, the main task in the future is to further reduce the number of parameters and computation of the model in order to improve the real-time detection speed. This will provide a more valuable reference for intelligent management of weed control in farmland.  
Conceptualization, H.Y. and Y.L.; methodology, Y.L.; software, Y.L., F.J. and Y.J.; validation, T.D., H.X. and L.Y.; formal analysis, Y.J., F.J. and Z.M.; investigation, J.G., T.D. and Y.Q.; resources, H.Y., T.D. and Y.L.; data curation, L.Y., J.G. and Y.Q.; writing--original draft preparation, Y.L.; writing--review and editing, H.Y., F.J. and Y.L.; visualization, Z.M. and H.X.; supervision, H.Y.; project administration, H.Y. and Y.J.; funding acquisition, H.Y. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (U1833119); the Humanities and Social Science Research Foundation of Ministry of Education of China (22 YJAZH038); the Ministry of Education Industry-University Cooperation Education Project (231106627155856); 2025 ESI (Engineering) (01003009); Hubei Provincial Natural Science Foundation (2025 AFC122); the School Enterprise Cooperation Project (No.whpu-2024-kj-4582, No.whpu-2024-kj-4639).  
Not applicable.  
These datasets can be found at [[https://universe.roboflow.com/universidade-federal-de-rondonpolis-phpbg/dados_tcc/](https://universe.roboflow.com/universidade-federal-de-rondonpolis-phpbg/dados_tcc/)]([https://universe.roboflow.com/universidade-federal-de-rondonpolis-phpbg/dados_tcc/](https://universe.roboflow.com/universidade-federal-de-rondonpolis-phpbg/dados_tcc/)) (accessed on 8 June 2024) and [[https://data.mendeley.com/datasets/8](https://data.mendeley.com/datasets/8) kjcztbjz2/2]([https://data.mendeley.com/datasets/8](https://data.mendeley.com/datasets/8) kjcztbjz2/2) (accessed on 8 June 2024).  
Author [PERSON] was employed by the company BiSiCloud (Wuhan) Information Technology Co., Ltd.; and author [PERSON] was employed by the company Zhengzhou Xinsiqi Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.