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Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

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Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning.

Mengyuan Chen1, Chenfeng Lin2, Yongqi Sun3

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Plants (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

This study introduces a novel hyperspectral imaging technique for rapid and accurate sex determination in Ginkgo biloba trees. The developed method achieves high accuracy, offering a valuable tool for managing this important dioecious species.

Keywords:
Ginkgo bilobahyperspectral imagingleaf morphologymachine learningsex identification

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Area of Science:

  • Plant Science
  • Remote Sensing
  • Machine Learning

Background:

  • Ginkgo biloba L. is a valuable, rare dioecious species cultivated worldwide.
  • Accurate sex determination is crucial for Ginkgo cultivation and ecological management.
  • Existing methods for sex determination can be time-consuming and labor-intensive.

Purpose of the Study:

  • To develop a rapid and effective method for determining the sex of Ginkgo biloba using hyperspectral imaging.
  • To establish a standard technique framework for sex classification in dioecious plants.

Main Methods:

  • Hyperspectral imaging of green and yellow Ginkgo leaves at different growth stages.
  • Development of classification models using RGB images, spectral features, and fused spectral-image features.
  • Application of machine learning algorithms including ResNet101, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Subspace Discriminant Analysis (SDA).
  • Proposal of a two-stage Period-Predetermined (PP) method for enhanced classification.

Main Results:

  • ResNet101 achieved 90.27% accuracy on RGB data.
  • Machine learning models showed high prediction accuracies: SVM and SDA (87.35%) for green leaves, LDA (98.85%) for yellow leaves.
  • The fused spectral-image dataset with the PP method reached an overall accuracy of 96.30% on the prediction set.

Conclusions:

  • Hyperspectral imaging provides an efficient and accurate method for Ginkgo biloba sex classification.
  • The developed technique framework offers a standardized approach for industrial and ecological applications.
  • This method has potential for classifying the sex of other dioecious plant species.