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A 3D point cloud and deep learning based automated process for quantifying multi-scale phenotypes in sliced bread.

Honghao Zhou1, Jungao Zhang2, Qin Zhou3

  • 1Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China; Collaborative Innovation Centre for Modern Crop Production, Co-sponsored by Province and Ministry, College of Agriculture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China.

Food Research International (Ottawa, Ont.)
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a 3D laser scanning technology for detailed bread analysis, capturing both overall slice structure and pore characteristics. The developed 3D-PoreSegNet model accurately extracts key phenotypic parameters for comprehensive bread quality assessment.

Keywords:
3D deep learning modelLine scanning laserPore 3D trait analysisSliced bread

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Area of Science:

  • Food Science and Technology
  • Computer Vision
  • Metrology

Background:

  • Traditional bread evaluation relies on limited 2D phenotypic analysis.
  • Accurate 3D structural data is crucial for comprehensive quality assessment.
  • Existing methods lack efficiency and detail in capturing bread's 3D characteristics.

Purpose of the Study:

  • To develop an innovative and inexpensive 3D laser scanning technology for bread phenotypic analysis.
  • To accurately capture and analyze the 3D structure of bread slices and their pore surfaces.
  • To introduce novel phenotypic parameters for enhanced bread quality evaluation.

Main Methods:

  • Utilized a 3D line-scan laser profiling sensor and a three-axis motion platform for data acquisition.
  • Developed a 3D-PoreSegNet segmentation model for separating bread and pore regions in 3D point cloud data.
  • Employed 2D projection, contour extraction, and inverse transformation for precise pore edge and 3D structure reconstruction.

Main Results:

  • Successfully reconstructed 3D bread and pore structures, extracting 13 phenotypic parameters with high accuracy.
  • Achieved high accuracy for total traits: height (97.3%), length (95.2%), width (95.6%), surface area (86.6%), volume (82.8%), symmetry (91.5%), uniformity (93.4%).
  • Demonstrated significant accuracy for pore parameters: max pore diameter (84.7%), max pore area (82.6%), pore count (87.1%), max pore depth (90.3%), max pore volume (83.7%), pore elongation (78.5%).

Conclusions:

  • The proposed 3D laser scanning technology effectively meets diverse phenotypic analysis requirements.
  • The developed Bread3D-Measure software facilitates rapid and accurate phenotypic analysis.
  • This innovative approach provides a robust framework for scientific bread quality assessment.