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Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray.

Yu Zhang1, Qianfan Liu1, Jing Chen2

  • 1School of Computer Science and Technology, Hainan University, Haikou, China.

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|March 9, 2023
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Summary

This study introduces an intelligent system using Computed Tomography (CT) to create 3D quantitative imaging models of coconuts. This non-destructive method aids in understanding coconut development and optimizing cultivation.

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Intelligent coconut analysisNon-invasivePoint cloudQuantitative imaging model

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Area of Science:

  • Agricultural Science
  • Imaging Technology
  • Computational Biology

Background:

  • Coconut's complex structure and long development cycle hinder internal observation.
  • Nondestructive methods are crucial for monitoring coconut growth and detecting issues.
  • Existing techniques face challenges due to the coconut's protective layers and internal environment.

Purpose of the Study:

  • To develop an intelligent system for non-destructive, 3D quantitative imaging of coconut internal development.
  • To establish a comprehensive coconut image dataset for research.
  • To create a tool for accurate structural data acquisition and analysis of coconuts.

Main Methods:

  • Utilized spiral Computed Tomography (CT) scanning to acquire cross-sectional coconut images.
  • Developed a 3D point cloud model by extracting 3D coordinates and RGB values.
  • Applied cluster denoising to refine the point cloud model for accurate representation.

Main Results:

  • Created the "CCID" dataset with 37,950 non-destructive internal growth maps.
  • Built an intelligent system capable of generating 3D point cloud maps from coconut images.
  • Demonstrated high accuracy in quantitative observation of coconut internal structures over three months.

Conclusions:

  • The 3D quantitative imaging model accurately captures internal coconut development.
  • The intelligent system supports growers with internal observations and data acquisition.
  • This technology provides decision-making support for improving coconut cultivation practices.