StomaAI: an efficient and user‐friendly tool for measurement of stomatal pores and density using deep computer vision

Summary Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment,...

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Bibliographic Details
Published in:The New phytologist 2023-04, Vol.238 (2), p.904-915
Main Authors: Sai, Na, Bockman, James Paul, Chen, Hao, Watson‐Haigh, Nathan, Xu, Bo, Feng, Xueying, Piechatzek, Adriane, Shen, Chunhua, Gilliham, Matthew
Format: Article
Language:English
Subjects:
applied deep learning
Arabidopsis
Cereal crops
Computer vision
Computers
convolutional neural network
Density
Humans
Inspection
Measurement
Microscopy
Phenotype
phenotyping
Plant physiology
Plant Stomata - physiology
Stomata
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Summary:Summary Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment, and can represent a significant bottleneck to research pipelines. To alleviate this, we introduce StomaAI (SAI): a reliable, user‐friendly and adaptable tool for stomatal pore and density measurements via the application of deep computer vision, which has been initially calibrated and deployed for the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass). SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. SAI boosts the number of images that can be evaluated in a fraction of the time, so can obtain a more accurate representation of stomatal traits than is routine through manual measurement. An online demonstration of SAI is hosted at https://sai.aiml.team, and the full local application is publicly available for free on GitHub through https://github.com/xdynames/sai‐app.
ISSN:0028-646X
1469-8137
DOI:10.1111/nph.18765