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A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem

文献类型： 外文期刊

作者： Du, Jianjun 1 ; Zhang, Ying 1 ; Lu, Xianju 1 ; Zhang, Minggang 1 ; Wang, Jinglu 1 ; Liao, Shengjin 1 ; Guo, Xinyu 1 ; Zhao, Chunjiang 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

关键词： Deep learning; Maize stem; Phenotyping; Semantic segmentation; Vascular bundle

期刊名称：CROP JOURNAL （ 影响因子：4.647； 五年影响因子：5.781 ）

ISSN： 2095-5421

年卷期： 2022 年 10 卷 5 期

页码：

收录情况： SCI

摘要： Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distri-bution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography (CT) images of stem intern -odes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy (R2) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bun-dles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.(c) 2021 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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