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Detection of Pine Cones in Natural Environment Using Improved YOLOv4 Deep Learning Algorithm.

Ze Luo1,2, Yizhuo Zhang1, Keqi Wang1

  • 1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

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This study introduces an improved YOLOv4 algorithm for accurate pine cone detection in forests. The enhanced model achieves high precision and recall, enabling efficient yield estimation and automated harvesting.

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Machine Learning

Background:

  • Accurate pine cone detection is crucial for yield estimation and automated harvesting in natural environments.
  • Complex backgrounds and small target sizes present significant challenges for current pine cone detection methods.

Purpose of the Study:

  • To develop an improved You Only Look Once (YOLO) version 4 algorithm for enhanced pine cone detection.
  • To address the challenges of complex backgrounds and tiny targets in natural pine forests.

Main Methods:

  • Utilized crawler technology to expand the pine cone image dataset.
  • Integrated DenseNet (densely connected convolution network) into YOLOv4 for improved feature reuse and performance.
  • Pruned the backbone network to reduce computational complexity while maintaining output dimensions.
  • Designed an improved neck network with scale-equalizing pyramid convolution (SEPC) for effective multi-scale feature fusion.

Main Results:

  • The improved YOLOv4 model demonstrated superior performance compared to the original YOLOv4.
  • Achieved average precision of 96.1%, recall of 90.1%, and AP of 95.8%.
  • Reduced model computational load by 21.2% while meeting real-time detection speed requirements.

Conclusions:

  • The proposed improved YOLOv4 algorithm offers a robust and efficient solution for pine cone detection.
  • This research provides a valuable technical reference for agricultural applications like yield estimation and automated pine cone harvesting.