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StomataCounter: a neural network for automatic stomata identification and counting.

Karl C Fetter1,2, Sven Eberhardt3, Rich S Barclay2

  • 1Department of Plant Biology, University of Vermont, Burlington, VT, 05405, USA.

The New Phytologist
|May 7, 2019
PubMed
Summary
This summary is machine-generated.

StomataCounter offers an automated method for identifying and counting plant stomata using deep learning. This tool enhances plant biology research by providing accurate, user-friendly analysis of stomatal density from microscopic images.

Keywords:
computer visionconvolutional deep learningneural networkphenotypingstomata

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Area of Science:

  • Plant Biology
  • Computational Biology
  • Image Analysis

Background:

  • Stomata are crucial for plant physiology and are frequently studied.
  • Current methods for stomatal phenotyping, such as manual counting, are time-consuming and lack automation.
  • Accurate stomatal density estimation is vital for understanding plant responses.

Purpose of the Study:

  • To develop a user-friendly, fully automated system for identifying and counting stomata.
  • To overcome the limitations of manual stomatal counting in plant biology research.
  • To provide a robust tool for estimating stomatal density across diverse plant species.

Main Methods:

  • Development of StomataCounter, an automated system employing a deep convolutional neural network (CNN).
  • Utilized a human-in-the-loop approach for training and refining the CNN model.
  • Trained the network on a taxonomically diverse dataset of microscopic plant images.

Main Results:

  • Achieved 98.1% identification accuracy on Ginkgo scanning electron microscopy (SEM) images.
  • Demonstrated 94.2% transfer accuracy on previously untrained plant species.
  • The StomataCounter system provides a significant improvement over manual counting methods.

Conclusions:

  • StomataCounter offers a highly accurate and automated solution for stomatal identification and counting.
  • The system's high transfer accuracy facilitates its application across various plant species.
  • A publicly accessible website (http://www.stomata.science/) is provided to promote adoption and further research.