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Type: Journal article
Title: StomaAI: an efficient and user-friendly tool for measurement of stomatal pores and density using deep computer vision
Author: Sai, N.
Bockman, J.P.
Chen, H.
Watson-Haigh, N.
Xu, B.
Feng, X.
Piechatzek, A.
Shen, C.
Gilliham, M.
Citation: New Phytologist, 2023; 238(2):904-915
Publisher: Wiley
Issue Date: 2023
ISSN: 0028-646X
Statement of
Na Sai, James Paul Bockman, Hao Chen, Nathan Watson-Haigh, Bo Xu, Xueying Feng, Adriane Piechatzek, Chunhua Shen, and Matthew Gilliham
Abstract: 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, and the full local application is publicly available for free on GitHub through
Keywords: applied deep learning
computer vision
convolutional neural network
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.
DOI: 10.1111/nph.18765
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Appears in Collections:Computer Science publications

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