Assessing the Storage Root Development of Cassava with a New Analysis Tool

Abstract

Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.

Plant phenotyping relevance

根系画像から根径分布などの表現型形質を自動抽出する新手法を開発し、仮想モデルで検証しており、表現型取得・解析が研究の中心である。

Code and data availability

The paper's root diameter analysis software is publicly available on the authors' GitLab (grow-screen-field). The phenotype/image data are deposited on Zenodo (doi: 10.5281/zenodo.5883368), but no Zenodo URL is in the allowed list, so only the code asset is reported. Paraview and Detectron2 are generic third-party tool