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StomataCounter: a deep learning method applied to automatic stomatal identification and counting

Karl C. Fetter, Sven Eberhardt, Rich S. Barclay, Scott Wing, Stephen R. Keller
doi: https://doi.org/10.1101/327494
Karl C. Fetter
aDepartment of Plant Biology, University of Vermont
bDepartment of Paleobiology, Smithsonian Institution, National Museum of Natural History
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Sven Eberhardt
cAmazon.com, Inc
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Rich S. Barclay
bDepartment of Paleobiology, Smithsonian Institution, National Museum of Natural History
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Scott Wing
bDepartment of Paleobiology, Smithsonian Institution, National Museum of Natural History
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Stephen R. Keller
aDepartment of Plant Biology, University of Vermont
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ABSTRACT

  • Stomata fulfill an important physiological role and are often phenotyped by researchers in many fields. Currently, no fully automated method exists to perform this task. Researchers typically rely on manual counts of stomata, which is an error-prone method and difficult to reproduce.

  • We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify pores in a variety of different microscopic images. We used a human-in-the-loop approach to train and refine a neural network on a large variety of microscopic images, which helps us achieve robust detection among a number of datasets.

  • Our network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species.

  • To facilitate adoption of the method, we make a web tool available under http://www.stomata.science/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted May 21, 2018.
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StomataCounter: a deep learning method applied to automatic stomatal identification and counting
Karl C. Fetter, Sven Eberhardt, Rich S. Barclay, Scott Wing, Stephen R. Keller
bioRxiv 327494; doi: https://doi.org/10.1101/327494
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StomataCounter: a deep learning method applied to automatic stomatal identification and counting
Karl C. Fetter, Sven Eberhardt, Rich S. Barclay, Scott Wing, Stephen R. Keller
bioRxiv 327494; doi: https://doi.org/10.1101/327494

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