A Novel Image-Analysis Toolbox Enabling Quantitative Analysis of Root System Architecture    

Abstract

We present in this paper a novel, semiautomated image-analysis software to streamline the quantitative analysis of root growth and architecture of complex root systems. The software combines a vectorial representation of root objects with a powerful tracing algorithm that accommodates a wide range of image sources and quality. The root system is treated as a collection of roots (possibly connected) that are individually represented as parsimonious sets of connected segments. Pixel coordinates and gray level are therefore turned into intuitive biological attributes such as segment diameter and orientation as well as distance to any other segment or topological position. As a consequence, user interaction and data analysis directly operate on biological entities (roots) and are not hampered by the spatially discrete, pixel-based nature of the original image. The software supports a sampling-based analysis of root system images, in which detailed information is collected on a limited number of roots selected by the user according to specific research requirements. The use of the software is illustrated with a time-lapse analysis of cluster root formation in lupin (Lupinus albus) and an architectural analysis of the maize (Zea mays) root system. The software, SmartRoot, is an operating system-independent freeware based on ImageJ and relies on cross-platform standards for communication with data-analysis software.

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

“…In contrast, Lobet et al (2011) and Naeem et al (2011) work directly on the original image data. These data contain more information and, therefore, can better seize small-scale image features and thus work with high precision on small root systems.…”

“…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.…”

Water: the most important ‘molecular’ component of water stress tolerance research

“…Thus, progress will be achieved by putting more focus on the distribution of roots in the soil profile, and an investigation of the role of roots in water stress adaptation may involve tomographic measurements of the root system in situ (e.g. Mooney et al 2012) or an examination of root models that take into account rooting architecture (Lobet et al 2011). However, measuring such a fine rooting differences at depth is a challenge, and although destructive measurements provide static data, they do not provide data on the quantities or kinetics of water extraction.…”

Analyzing Lateral Root Development: How to Move Forward

“…These include growth on moistened germination paper rolls or pouches, sand rhizotrons, rhizoboxes, in compost followed by washing, soil columns and gelbased systems where phenotypic effects can be imaged using flatbed scanners, digital cameras, lasers, or even x-ray computed tomography (CT) (Hetz et al, 1996;Whiting et al, 2000;Bengough et al, 2004;Fang et al, 2009;French et al, 2009;Gregory et al, 2009;Hammond et al, 2009;Iyer-Pascuzzi et al, 2010;Trachsel et al, 2010;Tracy et al, 2010Tracy et al, , 2011Chapman et al, 2011;Lobet et al, 2011;Lucas et al, 2011). Magnetic resonance imaging (for noninvasive analysis of root structures) and positron emission tomography (for analysis of carbon transport and accumulation) can be combined to study the dynamics of structure-function relationships of roots in real soils in a noninvasive manner .…”