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Inter-row navigation line detection for cotton with broken rows.

Xihuizi Liang1, Bingqi Chen2, Chaojie Wei3

  • 1Institute of intelligent manufacturing, Suzhou Chien-Shiung Institute of Technology, Suzhou, Jiangsu, China.

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|July 2, 2022
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Summary

This study presents a new algorithm for detecting navigation lines in seedling crops, improving accuracy for autonomous agricultural vehicles. The method effectively handles challenges like missing plants and wheel ruts, crucial for intelligent agriculture.

Keywords:
Autonomous navigationCrop rows detectionIntra-row lineMachine vision

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Robotics

Background:

  • Autopilot technology enhances path planning and labor productivity in agriculture.
  • Accurate navigation line detection is vital for self-driving vehicles in intelligent agriculture.
  • Existing methods struggle with seedling crops, especially double-row sown ones, due to missing plants and wheel rut interference.

Purpose of the Study:

  • To develop an effective algorithm for detecting navigation lines in wide and narrow row cotton crops at the seedling stage.
  • To address challenges posed by missing seedlings and wheel rut interference in seedling crop row extraction.
  • To improve the accuracy and reliability of autonomous navigation for agricultural machinery.

Main Methods:

  • A novel algorithm combining edge detection and OTSU thresholding to identify seedling row contours.
  • Utilizing least squares fitting to determine the navigation line within the gap of two narrow cotton rows.
  • Testing the algorithm on cotton, maize, and soybean seedling images under various lighting conditions.

Main Results:

  • Achieved high accuracy rates: 99.2% for cotton, 98.1% for maize, and 98.4% for soybean.
  • Demonstrated fast processing times, averaging 6.63-6.97 ms per frame.
  • Exhibited low deviation: 2 cm lateral and 0.57 degrees heading, indicating precise navigation.

Conclusions:

  • The proposed algorithm offers state-of-the-art performance for seedling row detection.
  • The method is robust to shadow interference and variations in crop row growth, ensuring normal operational speeds.
  • Applicable as a reference for navigation line fitting in diverse crops and complex agricultural environments.