pyRootHair: Machine learning accelerated software for high-throughput phenotyping of plant root hair traits
Ian Tsang, Lawrence Percival-Alwyn, Stephen Rawsthorne, James Cockram, Fiona Leigh, Jonathan A Atkinson
TL;DR
pyRootHair is a machine learning tool that automates the analysis of plant root hair traits from microscope images, enabling high-throughput phenotyping.
Contribution
pyRootHair introduces an AI-powered, high-throughput software for automated root hair trait quantification in multiple plant species.
Findings
pyRootHair can process over 600 images per hour, revealing significant variation in root hair traits among wheat cultivars.
Root hair profiles fall into two distinct shape categories, with correlations between different traits observed.
The software is applicable to multiple plant species including oat, rice, teff, and tomato.
Abstract
Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been 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. We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope 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…
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Taxonomy
TopicsPlant Molecular Biology Research · Plant nutrient uptake and metabolism · Plant responses to water stress