PT  - JOURNAL ARTICLE
AU  - Na Sai
AU  - James Paul Bockman
AU  - Hao Chen
AU  - Nathan Watson-Haigh
AU  - Bo Xu
AU  - Xueying Feng
AU  - Adriane Piechatzek
AU  - Chunhua Shen
AU  - Matthew Gilliham
TI  - SAI: Fast and automated quantification of stomatal parameters on microscope images
AID  - 10.1101/2022.02.07.479482
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.02.07.479482
4099  - http://biorxiv.org/content/early/2022/02/10/2022.02.07.479482.short
4100  - http://biorxiv.org/content/early/2022/02/10/2022.02.07.479482.full
AB  - Using microscopy to investigate stomatal behaviour is a common technique in plant physiology research. Manual inspection and measurement of stomatal features is a low throughput process in terms of time and human effort, which relies on expert knowledge to identify and measure stomata accurately. This process represents a significant bottleneck in research pipelines, adding significant researcher time to any project that requires it. To alleviate this, we introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision. We evaluated the reliability of predicted measurements: SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. Hence, SAI boosts the number of images that biologists can evaluate in a fraction of the time so is capable of obtaining more accurate and representative results.Competing Interest StatementThe authors have declared no competing interest.