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Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features.

Yuchen Kong1,2, Shenghui Fang1,2, Xianting Wu2,3

  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

Sensors (Basel, Switzerland)
|January 1, 2020
PubMed
Summary

This study introduces an automatic method for extracting rice thickness using binocular stereovision and edge features. The novel approach accurately measures rice thickness, aiding crop phenotyping research and quality assessment.

Keywords:
crop phenotypingdigital image processingphotogrammetryrice grainthickness

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Metrology

Background:

  • Accurate phenotyping parameters, like rice thickness, are crucial for rice quality assessment and research.
  • Traditional methods for 3D reconstruction face challenges with textureless objects like rice, hindering accurate measurement.
  • Developing automated, precise methods for extracting these parameters is essential for agricultural advancements.

Purpose of the Study:

  • To propose an automatic method for extracting rice thickness using binocular stereovision.
  • To overcome the limitations of texture-based matching in 3D reconstruction for rice.
  • To provide a reliable technique for phenotyping parameter extraction in crops.

Main Methods:

  • Utilized binocular stereovision principles for 3D reconstruction.
  • Employed edge shape features, rather than texture, for matching corresponding points on the rice edge.
  • Calculated rice edge height via space intersection and derived thickness from average edge height.

Main Results:

  • The proposed method successfully extracted rice thickness with errors within 0.1 mm, meeting national industry standards.
  • Experiments validated the effectiveness of using edge features for thickness extraction in rice and other grains.
  • The algorithm demonstrated high accuracy across six different types of rice or grain samples.

Conclusions:

  • Edge features are effective for accurate rice thickness extraction.
  • The developed algorithm provides a robust and validated solution for phenotyping parameter measurement.
  • This research offers significant technical support for crop researchers in automated phenotyping.