journal article·2025-04-13Open access

Slim-YOLO: An Improved Sugarcane Tail Tip Recognition Algorithm Based on YOLO11n for Complex Field Environments

Chunming Wen, Yang Cheng, Shangping Li, Leilei Liu, Qingquan Liang, Kaihua Li, Youzong Huang

Applied Sciences· Volume 15· Issue 8

Abstract

Accurate identification of the sugarcane tail tip is crucial for the real-time automation control of the harvester’s cutting device, improving harvesting efficiency, and reducing impurity rates. This paper proposes Slim-YOLO, an improved YOLO11n-based algorithm incorporating a lightweight RepViT backbone, an ELANSlimNeck neck structure, and the Unified-IoU (UIoU) loss function. Experimental results on the sugarcane tailing dataset show that Slim-YOLO achieves an mAP50 of 92.2% and mAP50:95 of 48.2%, outperforming YOLO11n by 8.2% and 6.1%, respectively, while reducing parameters by 48.4%. The enhanced accuracy and lightweight design make it suitable for practical deployment, offering theoretical and technical support for the automation control of sugarcane harvesters.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Sugarcane Cultivation and Processing · Agricultural Engineering and Mechanization · Vibration and Dynamic Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Guangxi Key Laboratory of Hybrid Computing and Integrated Circuit Design and Analysis, School of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China

Guangxi Zhuang Autonomous Region Intelligent Visual Collaborative Robot Engineering Research Center, Nanning 530006, China

School of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China

Guangxi University Engineering Research Center for Multimodal Information Intelligent Sensing Processing and Application, Nanning 530006, China

School of Physics and Electronic Information, Guangxi University for Nationalities, Nanning 530006, China

State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Nanning 530004, China

Funding

Guangxi Science and Technology Major Project · Open Project of Key Laboratory of Artificial Intelligence and Information Processing in Guangxi Colleges and Universities · Key Project of Teaching Reform of the Guangxi Zhuang Autonomous Region in 2022 · Project of Reform of Degree and Postgraduate Education in Guangxi in 2023 · 2020 Guangxi Degree and Postgraduate Education Reform Project

References (24)

Show source-provided references
1. Several Issues in the Mechanization of Sugarcane Production in Main Producing Areas
2. Department of Development Planning (2009). Compilation of the New Round of Advantageous Agricultural Products Regional Layout Planning, China Agriculture Press.
3. Lu, P. (2022). Investigation on the Development Status of Sugarcane Mechanized Harvest in Guangxi and Influencing Factors. [Master’s Thesis, Guangxi University].
4. Design and Test of the Hydraulic System of the Top Cutting Device for Small Sugarcane Harvester
5. Xia, A. (2023). Research on Cane Tip Recognition Method Based on Machine Vision. [Master’s Thesis, Guilin University of Technology].
6. Study on Image Segmentation Method of Sugarcane Tip in Complex Environment
7. Identification and Height Localization of Sugarcane Tip Bifurcation Points in Complex Environments Based on Improved YOLO V5s
8. Height Estimation of Sugarcane Tip Cutting Position Based on Multimodal Alignment and Depth Image Fusion10.1016/j.biosystemseng.2024.05.005 ↗
9. Militante, S.V., Gerardo, B.D., and Medina, R.P. (2019, January 3–6). Sugarcane Disease Recognition Using Deep Learning. Proceedings of the 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), IEEE, Yunlin, China.10.1109/ECICE47484.2019.8942686 ↗
10. A New Design of Sugarcane Seed Cutting Systems Based on Machine Vision10.1016/j.compag.2020.105611 ↗
11. Sugarcane Internode Recognition Based on Local Mean Values
12. Khanam, R., and Hussain, M. (2025, March 03). YOLOv11: An Overview of the Key Architectural Enhancements. Available online: https://doi.org/10.48550/arXiv.2410.17725.
13. Wang, A., Chen, H., Lin, Z., Han, J., and Ding, G. (2024, January 16). Rep ViT: Revisiting Mobile CNN From ViT Perspective. Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA.10.1109/CVPR52733.2024.01506 ↗
14. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information10.1007/978-3-031-72751-1_1 ↗
15. Slim-Neck by GSConv: A Lightweight-Design for Real-Time Detector Architectures10.1007/s11554-024-01436-6 ↗
16. Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation10.1109/TCYB.2021.3095305 ↗
17. Luo, X., Cai, Z., Shao, B., and Wang, Y. (2025, March 03). Unified-IoU: For High-Quality Object Detection. Available online: https://doi.org/10.48550/arXiv.2408.06636.
18. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–23). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
19. Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., and Peng, Y. (2024). Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases. Comput. Biol. Med., 170.10.1016/j.compbiomed.2024.107917 ↗
20. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
21. Lv, W., Xu, S., Zhao, Y., Wang, G., Wei, J., Cui, C., Du, Y., Dang, Q., and Liu, Y. (2024, January 16–22). DETRs Beat YOLOs on Real-time Object Detection. Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.
22. Sohan, M., Sai Ram, T., and Rami Reddy, C.V. (2024). A review on yolov8 and its advancements. International Conference on Data Intelligence and Cognitive Informatics, Springer.10.1007/978-981-99-7962-2_39 ↗
23. Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (2024). Yolov9: Learning what you want to learn using programmable gradient information. European Conference on Computer Vision, Springer Nature.10.1007/978-3-031-72751-1_1 ↗
24. Yolov10: Real-time end-to-end object detection