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Cotton stubble detection based on wavelet decomposition and texture features.

Yukun Yang1,2, Jing Nie1,2, Za Kan1,2

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China.

Plant Methods
|November 3, 2021
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Summary
This summary is machine-generated.

This study developed a visual navigation system for cotton residue film recovery, improving efficiency. The system uses texture features and wavelet decomposition for accurate cotton stubble detection, enhancing agricultural practices.

Keywords:
Fusion featureMachine visionStubbleTexture featureVisual defect detectionWavelet decomposition

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Image Processing

Background:

  • Residual film pollution in cotton fields poses a significant environmental challenge.
  • Current manual recycling methods are inefficient and labor-intensive.
  • Developing automated visual navigation systems is crucial for improving work efficiency.

Purpose of the Study:

  • To develop a robust cotton stubble detection algorithm for a visual navigation system.
  • To enhance the efficiency and reliability of residual film recovery in cotton fields.

Main Methods:

  • Extraction of texture features including GLCM, GLRLM, and LBP from stubble, film, and leaf images.
  • Classification using Random Forest, Back Propagation Neural Network, and Support Vector Machine models.
  • Investigation of texture features from wavelet decomposition coefficients for improved classification.

Main Results:

  • The Back Propagation Neural Network classifier with GLCM texture features from original images showed optimal performance.
  • Combining original image texture features with vertical coefficient texture features from coif3 wavelet decomposition yielded the best classification results.
  • This combined approach increased classification accuracy by 3.8%, sensitivity by 4.8%, and specificity by 1.2%.

Conclusions:

  • The developed algorithm effectively detects cotton stubble under various conditions (location, time, driving anomalies).
  • Fusion of wavelet coefficient texture features and original image texture features provides a valuable method for stubble detection.
  • This approach offers a reference for stubble detection in diverse crop cultivation systems.