pyRootHair: Machine Learning Accelerated Software for High-Throughput Phenotyping of Plant Root Hair Traits

Abstract

1 Abstract Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have been largely quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Here, we present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under two distinct shape categories, and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including arabidopsis ( Arabidopsis thaliana) , brachypodium ( Brachypodium distachyon ), medicago ( Medicago truncatula ), oat ( Avena sativa ), rice ( Oryza sativa ), teff ( Eragostis tef ) and tomato ( Solanum lycopersicum ). The application of pyRootHair enables users to rapidly screen large numbers of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variaton on plant performance.

Plant phenotyping relevance

根毛形態という植物形質を画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・検証しており、表現型取得手法が研究の中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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