Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learn...

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Bibliographic Details
Published inPlants (Basel) Vol. 10; no. 12; p. 2714
Main Authors Sultana, Syada Nizer, Park, Halim, Choi, Sung Hoon, Jo, Hyun, Song, Jong Tae, Lee, Jeong-Dong, Kang, Yang Jae
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 10.12.2021
MDPI
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Summary:Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation-a 1:1 ( / ) mixture of 95% ethanol and NaOCl (6-14%)-produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.
Bibliography:Syada Nizer Sultana and Halim Park contributed equally to this work.
ISSN:2223-7747
2223-7747
DOI:10.3390/plants10122714