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Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm.

Weiwei Hong1,2, Zenghong Ma1,3, Bingliang Ye1,3

  • 1Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

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An improved YOLOv5 algorithm enhances asparagus detection for intelligent harvesting. This computer vision model achieves 98.69% accuracy in complex environments, supporting automated agricultural machinery.

Keywords:
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Area of Science:

  • Computer Vision
  • Agricultural Robotics
  • Machine Learning

Background:

  • Intelligent machine harvesting of green asparagus requires efficient and accurate detection systems.
  • Existing algorithms may struggle with accuracy in complex environments and varying weather conditions.

Purpose of the Study:

  • To develop an improved YOLOv5 algorithm for accurate asparagus recognition and detection.
  • To enhance the algorithm's performance in complex environments for intelligent machine harvesting.

Main Methods:

  • Incorporated a coordinate attention (CA) mechanism into the YOLOv5 backbone.
  • Replaced PANet with BiFPN in the neck for improved feature propagation and reuse.
  • Constructed a diverse dataset of asparagus under various weather conditions.

Main Results:

  • The improved YOLOv5 model achieved a mean Average Precision (mAP) of 98.69% at an IoU threshold of 0.5.
  • This represents a 4.22% increase in mAP compared to the original YOLOv5 prototype.
  • The enhanced model demonstrated effective detection capabilities in complex and diverse conditions.

Conclusions:

  • The proposed improved YOLOv5 algorithm offers high accuracy for asparagus detection.
  • It provides robust technical support for intelligent machine harvesting of asparagus across different weather conditions and complex environments.