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.

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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.