RootGraph: a graphic optimization tool for automated image analysis of plant roots.

Jinhai Cai,Jason N Connor,Stanley J Miklavcic,Zhanghui Zeng,Pankaj Kumar,Chun Yuan Huang,Vanessa Melino

doi:10.1093/jxb/erv359

Abstract

This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions.

Highlights

Plant root development, root systems and their 3D architecture (RSA) have been subjects of extensive study for many decades (Gregory et al, 1987)
It is well known that RSA is under genetic control but can be significantly influenced by environmental factors [the genotype -by- environment (G×E) interaction] (Fitter, 2002; Masle, 2002); the phenotype corresponding to a specific genotype is environmentally dependent (Crossa, 2012; El-Soda et al, 2014)
Was the number of roots contained in an image of a plant root system quantified, but root tips belonging to primary roots were distinguished from those belonging to lateral roots

Summary

Introduction

Root systems and their 3D architecture (RSA) have been subjects of extensive study for many decades (Gregory et al, 1987). Specific studies have documented the effects of abiotic stress on plant growth and adaptation and identified quantitative trait loci (QTLs) associated with important root traits (Kamoshita et al, 2008). With this achieved, the screening of large mapping populations calls for highthroughput analysis methods to deal with the large number of samples possible through root plasticity (Ardiel et al, 2002; Kamoshita et al, 2008). An important, if not indispensable, component in any high-throughput phenotyping pipeline is a robust, accurate and fully automated image analysis tool

Methods

Results

Conclusion