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

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dc.contributor.authorSai, N.
dc.contributor.authorBockman, J.P.
dc.contributor.authorChen, H.
dc.contributor.authorWatson-Haigh, N.
dc.contributor.authorXu, B.
dc.contributor.authorFeng, X.
dc.contributor.authorPiechatzek, A.
dc.contributor.authorShen, C.
dc.contributor.authorGilliham, M.
dc.date.issued2023
dc.descriptionData source: Data availability, https://gitfront.io/r/jpb/u6BtFFMkNGCv/SAI-training
dc.description.abstractUsing 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.
dc.description.statementofresponsibilityNa Sai, James Paul Bockman, Hao Chen, Nathan Watson-Haigh, Bo Xu, Xueying Feng, Adriane Piechatzek, Chunhua Shen, and Matthew Gilliham
dc.identifier.citationNew Phytologist, 2023; 238(2):904-915
dc.identifier.doi10.1111/nph.18765
dc.identifier.issn0028-646X
dc.identifier.issn1469-8137
dc.identifier.orcidSai, N. [0000-0002-9482-6115]
dc.identifier.orcidBockman, J.P. [0000-0002-2840-2533]
dc.identifier.orcidXu, B. [0000-0002-7583-2384]
dc.identifier.orcidPiechatzek, A. [0000-0002-7958-5771]
dc.identifier.orcidShen, C. [0000-0002-8648-8718]
dc.identifier.orcidGilliham, M. [0000-0003-0666-3078]
dc.identifier.urihttps://hdl.handle.net/2440/137612
dc.language.isoen
dc.publisherWiley
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100008
dc.relation.granthttp://purl.org/au-research/grants/arc/DP210102828
dc.relation.granthttp://purl.org/au-research/grants/arc/LE190100080
dc.rights© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
dc.source.urihttps://doi.org/10.1111/nph.18765
dc.subjectapplied deep learning
dc.subjectcomputer vision
dc.subjectconvolutional neural network
dc.subjectphenotyping
dc.subjectstomata
dc.titleStomaAI: an efficient and user-friendly tool for measurement of stomatal pores and density using deep computer vision
dc.typeJournal article
pubs.publication-statusPublished

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