Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning Article Swipe
Syada Nizer Sultana
,
Halim Park
,
Sung Hoon Choi
,
Hyun Jo
,
Jong Tae Song
,
Jeong‐Dong Lee
,
Yang Jae Kang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/plants10122714
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/plants10122714
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 (v/v) 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/plants10122714
- https://www.mdpi.com/2223-7747/10/12/2714/pdf?version=1639134455
- OA Status
- gold
- Cited By
- 11
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200254186
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https://openalex.org/W4200254186Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/plants10122714Digital Object Identifier
- Title
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Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-10Full publication date if available
- Authors
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Syada Nizer Sultana, Halim Park, Sung Hoon Choi, Hyun Jo, Jong Tae Song, Jeong‐Dong Lee, Yang Jae KangList of authors in order
- Landing page
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https://doi.org/10.3390/plants10122714Publisher landing page
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https://www.mdpi.com/2223-7747/10/12/2714/pdf?version=1639134455Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2223-7747/10/12/2714/pdf?version=1639134455Direct OA link when available
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Artificial intelligence, Deep learning, Computer science, Chemistry, Machine learning, Biological system, BiologyTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 4, 2024: 5, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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