RRC ID |
70587
|
Author |
Toda Y, Tameshige T, Tomiyama M, Kinoshita T, Shimizu KK.
|
Title |
An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation.
|
Journal |
Front Plant Sci
|
Abstract |
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 (SD) between adaxial and abaxial surfaces and in stomatal size (SS) between wheat-related species of different ploidy. Utilizing such a platform is expected to accelerate research that involves all aspects of stomata phenotyping.
|
Volume |
12
|
Pages |
715309
|
Published |
2021-1-1
|
DOI |
10.3389/fpls.2021.715309
|
PMID |
34394171
|
PMC |
PMC8358771
|
IF |
4.402
|
Resource |
Wheat |
KU-2076
KU-199-1
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