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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on

Zhijie Qin1, Zhongfu Zhang1, Xiangdong Hua1

  • 1College of Engineering, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.

Scientific Reports
|February 25, 2022
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This study introduces a novel point cloud method for cereal grain analysis, improving efficiency and accuracy in measuring phenotypic traits like length and width. This technique aids crop breeding and genetic studies by offering a faster, more objective alternative to manual measurements.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Biotechnology

Background:

  • Cereal grains are vital global food sources.
  • Manual measurement of grain traits is inefficient and subjective.
  • Accurate phenotypic trait extraction is crucial for crop breeding and genetic analysis.

Purpose of the Study:

  • To develop and validate a novel point cloud-based method for cereal grain phenotypic trait extraction.
  • To assess the efficiency and accuracy of the proposed method compared to manual measurements.
  • To evaluate machine learning models for grain classification based on extracted traits.

Main Methods:

  • Acquisition of grain point cloud data using a structured light scanner.
  • Single grain segmentation via image preprocessing, plane fitting, and region growth clustering.
  • Calculation of grain dimensions (length, width, thickness, surface area, volume) using point cloud algorithms.
  • Application of machine learning models for filled/unfilled and variety identification.

Main Results:

  • The point cloud method achieved high accuracy with average errors of 2.07% (length), 0.97% (width), and 1.13% (thickness).
  • Measurement efficiency was significantly improved, averaging approximately 9.6 seconds per grain.
  • Machine learning models demonstrated high accuracy in filled/unfilled grain classification (90.18%) and indica/japonica identification (99.95%).

Conclusions:

  • The proposed point cloud method offers an efficient and effective approach for objective cereal grain phenotyping.
  • This technology has significant potential to advance crop breeding and genetic research.
  • Further research may be needed to improve variety identification accuracy.