A Volumetric Segmentation Method for Learning Structural Representations of Plant Roots in 3D X-Ray CT Scans

Abstract

Critical factors that determine crop yields are located underground, making them difficult to analyze. Traditionally, these factors have been measured by growing plants in clear media and measuring traits with visible imaging. Modern phenomics technologies use one or several imaging modalities to capture traits that reflect plant physiology or performance. Analytical techniques for plant phenomics are a crucial part of approaches to achieving desirable agronomic and biological traits. Advances in sensor technologies have paved the way for faster and more efficient plant phenotyping, with methods adapted from disciplines like high-resolution 3D X-Ray computed tomography (CT). A crucial step in their analysis is segmentation-the identification and classification of the scan's voxels as "root" or "non-root". Unlike roots in transparent mediums, roots in non-transparent mediums are difficult to segment from their surrounding materials as root and non-root voxels have overlapping CT values. The challenge we address is the development of neural-driven approaches for volumetric semantic segmentation of plant roots in 3D CT scans, and discuss subsequent trait extraction methods that enable the quantification of root systems and their traits in several agriculturally

Plant phenotyping relevance

3D X線CT画像から根をセグメンテーションし、根系形質を抽出・定量化する手法開発が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The supplied blocks contain only the title page and introduction of the preprint. There is no data availability statement, code deposit, repository URL, or supplement reference describing the paper's CT scans, annotations, or segmentation code. No paper-specific public asset can be identified from the supplied text.

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