4.7 Article

StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model

期刊

PLANT BIOTECHNOLOGY JOURNAL
卷 20, 期 3, 页码 577-591

出版社

WILEY
DOI: 10.1111/pbi.13741

关键词

living stomata; stomata detection; pore segmentation; stomatal density; pore traits; improved CV model; deep learning

资金

  1. National Key Research and Development Program [2020YFD1000904-1-3]
  2. National Natural Science Foundation of China [31770397]
  3. Major science and technology projects in Hubei Province
  4. Fundamental Research Funds for the Central Universities [2662020GXPY010, 2662020ZKPY017, 2021ZKPY006]
  5. Huazhong Agricultural University [SZYJY2021005, SZYJY2021007]
  6. Shenzhen Institute of agricultural genomics [SZYJY2021005, SZYJY2021007]

向作者/读者索取更多资源

A new method was proposed to automatically and nondestructively measure stomatal traits, utilizing portable microscopes and different models to detect and extract stomatal features; The study found that mutant plants exhibited higher resilience in stomatal traits compared to wild-type under different conditions; The development of a method for measuring stomatal traits across multiple species and a user-friendly web portal were highlighted.
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 40x lens attached to a smartphone and ProScope HR2 with a 400x 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-2) 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.

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