Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

Abstract

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.

Plant phenotyping relevance

根系画像から建築形質を抽出する半自動画像解析と機械学習手法の開発であり、表現型取得の高速化と形質推定を中心に扱っているため含める。

Code and data availability

The paper's root image datasets, RSML annotations, and genotype mapping data are openly deposited in GigaScience's GigaDB (DOI 10.5524/100346), and the authors' PRIMAL Random Forest analysis application is publicly available at https://plantmodelling.github.io/primal/. Both are paper-specific, public, and actionable.