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.
Highlights
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
It is widely accepted that root system architecture (RSA) is a fundamental component of agricultural and natural ecosystems productivity (Lynch, 1995; Hammer et al, 2009; Hodge et al, 2009)
A panel of software have been implemented targeting specific traits and experimental constraints. These software can be assigned to manual, semiautomated, and fully automated methods according to the amount of user interaction
Summary
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. Recent progress in our understanding of the molecular bases of root growth and development in model systems (De Smet et al, 2006; Peret et al, 2009) and novel insights on the role of RSA in field resource capture (Draye et al, 2010) yield new prospects of manipulating RSA in crop species (de Dorlodot et al, 2007) This situation reinforces the need for robust, evolutive, and high-throughput root phenotyping hardware and software solutions. Users typically draw the skeleton of the root system using freehand graphical tools, as in DART (Le Bot et al, 2010) or Win RHIZO Tron (Regent Instruments, 2011) These methods exclude software-generated errors and should provide accurate estimation of most local and global traits, but they are highly time consuming.
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