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Sugarcane stem node detection with algorithm based on improved YOLO11 channel pruning with small target enhancement.

Chunming Wen1,2,3,4,5, Leilei Liu5, Shangping Li2,3,4,5

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This study introduces an improved YOLOv11 model for accurate sugarcane stem node detection, enhancing precision agriculture. The refined model significantly boosts detection accuracy and efficiency in complex field conditions.

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Accurate sugarcane stem node detection is vital for growth monitoring, precision harvesting, and breeding, but is challenged by complex field conditions like background interference and shadowing.
  • Existing models often struggle with detecting small objects and handling variations in scale and occlusion, limiting their effectiveness in real-world agricultural applications.

Purpose of the Study:

  • To develop an improved sugarcane stem node detection model based on YOLOv11 to overcome limitations in complex field environments.
  • To enhance the model's capability for detecting small objects, improving feature fusion, and refining bounding box regression for increased accuracy.

Main Methods:

  • Incorporation of the Attentional Scale Sequence Fusion (ASF-YOLO) mechanism into the YOLOv11 feature fusion layer.
  • Integration of a high-resolution P2 detection layer and a Lightweight Shared Detail-Enhanced Convolutional Detection Head (LSDECD) for improved small object detection and parameter efficiency.
  • Utilized soft-Non-Maximum Suppression (soft-NMS) with Shape-IoU for more accurate bounding box regression, addressing occlusion and illumination issues, followed by channel pruning to reduce complexity.

Main Results:

  • The proposed model achieved a mean average precision (mAP50) of 96.1% and mAP50:95 of 53.2% before pruning, outperforming the original YOLOv11n by 11.9% and 11.1%, respectively.
  • After channel pruning, the model still showed significant improvements with 10.8% and 9.3% higher mAP50 and mAP50:95, respectively, while reducing parameters to 279,778, model size to 1.3MB, and computational cost from 11.6 GFlops to 6.6 GFlops.

Conclusions:

  • The improved YOLOv11 model, incorporating ASF-YOLO, P2 layer, LSDECD, soft-NMS, and Shape-IoU, demonstrates superior performance in sugarcane stem node detection.
  • The model's efficiency and accuracy are maintained even after complexity reduction through channel pruning, making it suitable for practical precision agriculture applications.