An automated, high-throughput image analysis pipeline enables genetic studies of shoot and root morphology in carrot ( Daucus carota L.)

Abstract

Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.

Plant phenotyping relevance

ニンジンのシュート・根の形態形質を画像から自動抽出する高スループット解析基盤を開発し、手動測定との相関や反復性で検証しているため、表現型取得法が研究の中心である。

Code and data availability

The paper's Data Availability statement provides public, paper-specific assets: carrot plant images via a CyVerse download link, and authors' scripts for data processing, visualization, and QTL mapping on GitHub. Both are directly tied to this paper's phenotyping measurements and analysis.