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

Abstract

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 distribution 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 internodes. 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 bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.

Plant phenotyping relevance

CT画像からトウモロコシ茎の維管束形質を自動抽出する深層学習統合フェノタイピング手法を開発・検証しており、方法が研究の中心である。

Code and data availability

The paper describes a deep learning phenotyping pipeline for maize vascular bundles with CT images, an annotated dataset (1659 image pairs), and trained segmentation models, but no block provides public availability, deposit, or URL for the dataset, code, or models. The only supplementary-data mention is a generic link

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