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Field cabbage detection and positioning system based on improved YOLOv8n.

Ping Jiang1, Aolin Qi1, Jiao Zhong1

  • 1College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, 410128, China.

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Summary
This summary is machine-generated.

This study introduces an improved YOLOv8n object detection model for accurate cabbage identification and positioning in fields. The system achieves high precision, supporting targeted pesticide spraying applications.

Keywords:
CabbageLarge kernel convolutionsObject detectionSwin transformerYOLOv8n

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Robotics

Background:

  • Pesticide efficacy is crucial for crop yield and quality.
  • Targeted spraying enhances efficiency and environmental friendliness.
  • Accurate cabbage detection is challenging due to complex field conditions.

Purpose of the Study:

  • To develop an accurate cabbage detection and positioning system for targeted spraying.
  • To improve the performance of object detection models in complex agricultural environments.

Main Methods:

  • A YOLOv8n neural network (YOLOv8-cabbage) was selected and enhanced for cabbage detection.
  • Techniques included data augmentation, large kernel convolution, Swin transformer integration, and a nonlocal attention mechanism.
  • A Realsense depth camera was used for 3D positioning via pixelwise alignment of depth and color maps.

Main Results:

  • The enhanced YOLOv8-cabbage model achieved a mean average precision (mAP) of 93.9%, an increase from 88.8%.
  • The 3D coordinate positioning system demonstrated a low error (11.2 mm, 10.225 mm, 25.3 mm).
  • Real-time cabbage detection in complex environments was achieved.

Conclusions:

  • Accurate cabbage positioning was successfully achieved.
  • The developed system provides technical support for targeted spraying applications.
  • The system enables real-time detection in challenging field conditions.