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
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 ↗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 ↗16. Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
10.1109/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