National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), College of Engineering, Huazhong Agricultural University, Wuhan, China.
Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Plant Biotechnol J. 2022 Mar;20(3):577-591. doi: 10.1111/pbi.13741. Epub 2021 Nov 12.
To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R ) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%-6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.
为了实现对气孔特征的自动、无损测量，提出了一种新的气孔检测和提取方法。使用两台具有不同分辨率的便携式显微镜（TipScope 连接智能手机的 40×镜头和 ProScope HR2 的 400×镜头）获取玉米叶片中活气孔的图像。使用 FPN 模型检测 TipScope 图像中的气孔，并测量气孔数量和气孔密度。使用 Faster RCNN 模型检测 ProScope HR2 图像中的张开和关闭的气孔，并测量其数量。改进的 CV 模型用于分割张开气孔的孔，共测量了 6 个孔特征。与手动测量相比，这 6 个孔特征的平方相关系数（R）均高于 0.85，这些特征的平均绝对百分比误差（MAPE）为 0.02%-6.34%。在干旱和复水条件下，研究了野生型 B73 和突变体 Zmfab1a 之间的动态气孔变化。结果表明，Zmfab1a 在叶片气孔上比 B73 具有更高的弹性。此外，还测试了该方法来测量其他 9 个物种的叶片气孔特征。总之，开发了一种便携式、低成本的气孔表型分析方法，可准确、动态地测量活气孔的特征参数。还开发了一个开放访问和用户友好的网络门户，将来有望用于大规模的气孔表型分析。
