al document of web portal. File S2 Technical document of EXE software. Data Availability Statement The operating procedure for stomatal trait extraction is shown in Video S1 . The detailed technical documentation is given in Note S1 . The trained model, user guideline and all the labelled images of leaf stomata are available at http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action . The source codes are available from the first/corresponding author. The web portal of extracting stomatal traits was available at http://x40833180q.zicp.vip .
Open resource ↗plantphenomics.hzau.edu.cn · lines:638-643Unverified paper record
StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.
Plant biotechnology journal · 12 Nov 2021 · 10.1111/pbi.13741
Abstract
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 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.
Plant phenotyping relevance
生きた葉の気孔形質を画像取得・深層学習・セグメンテーションで自動抽出する手法を開発し、手動測定との比較検証とWebポータル提供まで行っており、植物フェノタイピング手法が中心である。
abstracta new method for detecting stomata and extracting stomatal traits was proposed.
abstractCompared 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%.
abstractIn conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed.
Code and data availability
The paper's Data Availability Statement deposits the trained stomata detection/segmentation models and all labelled leaf stomata images at a public Huazhong Agricultural University plant phenomics download portal, which directly supports this paper's phenotyping measurements. The analysis source codes are only 'availab
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.