Plant Transport and Signalling Lab, ARC Centre of Excellence in Plant Energy Biology, Waite Research Institute, Glen Osmond, SA, 5064, Australia.
School of Agriculture, Food and Wine, University of Adelaide, Adelaide, SA, 5064, Australia.
New Phytol. 2023 Apr;238(2):904-915. doi: 10.1111/nph.18765. Epub 2023 Feb 18.
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.
利用显微镜研究气孔行为在植物生理学研究中很常见。手动检查和测量气孔特征的方法通量低，需要专家知识才能准确记录气孔特征，需要大量的研究人员时间和投资，并且可能成为研究管道的一个重大瓶颈。为了解决这个问题，我们引入了 StomaAI（SAI）：一种可靠、用户友好且适应性强的工具，可通过应用深度学习进行气孔孔径和密度测量，该工具已经针对模式植物拟南芥（双子叶植物）和作物植物大麦（单子叶禾本科植物）进行了初步校准和部署。SAI 能够生成与人类专家一致的测量结果，并成功复制了已发表数据集的结论。SAI 可以在一小部分时间内评估更多的图像，因此可以比通过手动测量更准确地获得气孔特征的代表性。SAI 的在线演示托管在 https://sai.aiml.team 上，完整的本地应用程序可通过 https://github.com/xdynames/sai-app 在 GitHub 上免费获得。
