GiA Roots: software for the high throughput analysis of plant root system architecture

Abstract

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

“…Root systems growing in nutrient-enriched gellan gum were imaged in 360°view by a computer-controlled camera (17,20). The resulting image sets were uploaded automatically to a server for processing and phenotyping by GiA Roots, a free and extensible software package for the general phenotyping of plant root architecture (www.giaroots.org) (22). An average imaging session lasted 5 min and produced a rotational series of 40 images (9°apart), which were uploaded to a server for processing.…”

“…Images were processed in an automatic phenotyping pipeline centered on GiA Roots (22). Steps consisted of scaling, rotating, and cropping images as a set, creating a greyscale image, and then applying a double adaptive thresholding with preset parameters specific to our imaging conditions to produce binary foreground (root) or background (nonroot) (22).…”

“…The three methods we combined are (i) a method to use 2D rotational image series to estimate root traits (20); (ii) GiA Roots, a standalone, graphical user interface-based, freely distributed software with enhanced semiautomated image processing and 2D analysis features (22); and (iii) an implementation of an algorithm that generates 3D reconstructions from rotational image series taken in optical correction tanks (17) as described in Zheng et al (21). For this study, we constructed a platform with dual imaging setups on a massive table designed to reduce vibrations and maintain the precise calibration necessary for fully automated 3D reconstructions (the basic design is described in ref.…”

“…The integrated system leverages prior advances in the areas of hardware, imaging, software, and analysis (17,(20)(21)(22). We combined these methods into a semiautomated pipeline to reconstruct and phenotype a well-studied rice mapping population on days 12, 14, and 16 after planting in gellan gum medium.…”

Recovering Root System Traits Using Image Analysis Exemplified by Two-Dimensional Neutron Radiography Images of Lupine

“…The majority of imaging systems for root systems are designed for two-dimensional images, such as RootReader2D (Clark et al, 2013), GiA Roots (Galkovskyi et al, 2012), SmartRoot (Lobet et al, 2011), EZ-Rhizo (Armengaud et al, 2009), and Growscreen (Nagel et al, 2012). See also Le Bot et al (2010) for a review of available software.…”