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A Multi-Considered Seed Coat Pattern Classification of Allium L. Using Unsupervised Machine Learning.

Gantulga Ariunzaya1, Shukherdorj Baasanmunkh2, Hyeok Jae Choi2

  • 1Department of Computer Engineering, Changwon National University, Changwon 51140, Republic of Korea.

Plants (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

Unsupervised machine learning classified Allium seed coat patterns into new taxonomic groups. This method supports the development of novel classifications for Allium seed morphology.

Keywords:
Allium seed coatSEMnew groupingtesta sculptureunsupervised machine learning

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Area of Science:

  • Botany
  • Computational Biology
  • Machine Learning

Background:

  • Seed coat sculpture is a key taxonomic feature in plant identification.
  • Classifying Allium L. seed coat patterns is crucial for understanding species diversity.

Purpose of the Study:

  • To classify Allium L. seed coat patterns into new groups using unsupervised machine learning.
  • To evaluate the efficacy of different machine learning algorithms in seed coat pattern analysis.

Main Methods:

  • Scanning electron microscopy was used to capture images of Allium seed coat patterns.
  • Five unsupervised machine learning algorithms (K-means, K-means++, Minibatch K-means, Spectral, Birch) were applied.
  • Elbow and silhouette methods were used to determine optimal cluster numbers.

Main Results:

  • Seed coat patterns were categorized into seven anticlinal and five periclinal wall types.
  • Machine learning algorithms identified six distinct clusters (SI, SS, SM, NS, PS, PD).
  • A new clustering was proposed, with the 'strongly identical' (SI) group showing high consistency and 'PD' being an outlier.

Conclusions:

  • Unsupervised machine learning effectively supports the development of new taxonomic groups for Allium seed coat patterns.
  • The study demonstrates the potential of computational methods in plant taxonomy and morphology.