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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots.

Teng Miao1, Weiliang Wen2,3,4, Yinglun Li3,4

  • 1College of Information and Electrical Engineering, Shenyang Agricultural University, Dongling Road, Shenhe District, Liaoning Province, Shenyang 110161, China.

Gigascience
|May 8, 2021
PubMed
Summary

We developed Label3DMaize, a toolkit for semi-automatic 3D point cloud segmentation of maize shoots. This tool aids in accurate plant organ phenotyping and 3D reconstruction, overcoming current segmentation challenges for high-throughput analysis.

Keywords:
3D point cloudLabel3DMaizedata annotationmaize shootsegmentation

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Area of Science:

  • Plant science
  • Computer vision
  • Computational biology

Background:

  • 3D point clouds are crucial for plant structure and morphology studies.
  • Accurate plant organ segmentation from point clouds is essential for phenotype estimation and 3D reconstruction.
  • Current automatic and robust point cloud segmentation methods for plants are lacking, hindering high-throughput analysis.

Purpose of the Study:

  • To develop a novel point cloud segmentation algorithm for maize shoots.
  • To create a user-friendly toolkit for semi-automatic point cloud annotation and segmentation of maize shoots.
  • To improve the accuracy and efficiency of plant organ-level phenotyping.

Main Methods:

  • A top-to-down point cloud segmentation algorithm utilizing optimal transportation distance was developed.
  • The Label3DMaize toolkit was created for semi-automatic segmentation and annotation of maize shoots.
  • The process involves stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation.

Main Results:

  • Label3DMaize enables semi-automatic segmentation and annotation of maize shoots across different growth stages.
  • Segmentation of a single maize shoot takes approximately 4-10 minutes, with coarse segmentation requiring 10-20% of total time.
  • Coarse segmentation accuracy reached 97.2%, with fine segmentation offering greater detail, especially at organ connections.

Conclusions:

  • Label3DMaize integrates segmentation algorithms and manual operations for semi-automatic maize shoot point cloud processing.
  • The toolkit serves as a practical data annotation tool, facilitating deep learning-based segmentation research.
  • This approach is expected to advance automatic point cloud processing for diverse plant species.