論文

責任著者
2021年7月29日

An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation

Frontiers in Plant Science
  • Yosuke Toda
  • ,
  • Toshiaki Tameshige
  • ,
  • Masakazu Tomiyama
  • ,
  • Toshinori Kinoshita
  • ,
  • Kentaro K. Shimizu

12
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3389/fpls.2021.715309
出版者・発行元
Frontiers Media SA

Recent technical advances in the computer-vision domain have facilitated the development of various methods for achieving image-based quantification of stomata-related traits. However, the installation cost of such a system and the difficulties of operating it on-site have been hurdles for experimental biologists. Here, we present a platform that allows real-time stomata detection during microscopic observation. The proposed system consists of a deep neural network model-based stomata detector and an upright microscope connected to a USB camera and a graphics processing unit (GPU)-supported single-board computer. All the hardware components are commercially available at common electronic commerce stores at a reasonable price. Moreover, the machine-learning model is prepared based on freely available cloud services. This approach allows users to set up a phenotyping platform at low cost. As a proof of concept, we trained our model to detect dumbbell-shaped stomata from wheat leaf imprints. Using this platform, we collected a comprehensive range of stomatal phenotypes from wheat leaves. We confirmed notable differences in stomatal density (<italic>SD</italic>) between adaxial and abaxial surfaces and in stomatal size (<italic>SS</italic>) between wheat-related species of different ploidy. Utilizing such a platform is expected to accelerate research that involves all aspects of stomata phenotyping.

リンク情報
DOI
https://doi.org/10.3389/fpls.2021.715309
URL
https://www.frontiersin.org/articles/10.3389/fpls.2021.715309/full
ID情報
  • DOI : 10.3389/fpls.2021.715309
  • eISSN : 1664-462X

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