Pathoumthong Phetdalaphone, Zhang Zhen, Roy Stuart J, El Habti Abdeljalil
School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae, 5064, Australia.
The Waite Research Institute, Urrbrae, 5064, Australia.
Plant Methods. 2023 Mar 31;19(1):36. doi: 10.1186/s13007-023-01016-y.
Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato.
The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes.
We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
气孔是叶片表面的微小孔隙，对气体交换至关重要。气孔数量、大小和孔径是植物蒸腾作用和光合作用的关键决定因素，这些性状的变化会影响植物生长和生产力。目前筛选气孔表型的方法繁琐且通量不高。这阻碍了气孔生物学研究，并妨碍了培育具有优化气孔模式的抗逆作物的努力。我们开发了一种快速无损方法，用于对三种作物——小麦、水稻和番茄的气孔性状进行表型分析。
该方法包括两个步骤。第一步是使用手持显微镜在生长环境中对植物叶片表面进行无损图像采集；与其他方法需要数分钟相比，这个过程只需几秒钟。第二步是使用机器学习模型分析气孔特征，该模型可自动检测、计数并测量气孔数量、大小和孔径。机器学习模型检测气孔的准确率在88%至99%之间，具体取决于物种，使用机器学习模型测量的数量、大小和孔径与手动测量结果之间具有高度相关性。该快速方法被用于快速识别不同的气孔表型。
我们开发了一种将叶片表面快速无损成像与自动图像分析相结合的方法。该方法可提供关于气孔特征的准确数据，同时显著减少数据采集和分析时间。它可轻松用于在田间和可控环境中对大量植株的气孔进行表型分析。
