Slim-YOLO: An Improved Sugarcane Tail Tip Recognition Algorithm Based on YOLO11n for Complex Field Environments Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.3390/app15084286
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15084286
- https://www.mdpi.com/2076-3417/15/8/4286/pdf?version=1744533581
- OA Status
- gold
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409433753