A Novel Image-Analysis Toolbox Enabling Quantitative Analysis of Root System Architecture1[W][OA]

By G. Lobet, L. Pagès, X. Draye. Plant Physiology, 2011

521 citations recorded by the source corpus.

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.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/5cabb83cee3fc5b2dc9550e98e71fcc0331fa2dd

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Citations in the corpus

Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. (2012, methodology): “, 1993), imaging of washed roots with flatbed scanners, and software packages for analysing the washed images (French et al., 2009; Le Bot et al., 2010; Lobet et al., 2011).”
Plant phenotyping: from bean weighing to image analysis (2015, methodology): “With the development of suitable software such as SmartRoot [97] that allows for topology analyses, root system architecture can be analyzed in detail for branching angles etc. and growth kinematics of individual roots within the root system [87].”
RhizoVision Explorer: open-source software for root image analysis and measurement standardization (2021, unknown): “…connected root system images such as root crowns or seedling root systems grown on blue paper included EZ-Rhizo (Armengaud et al. 2009), SmartRoot (Lobet et al. 2011), RootNav (Pound et al. 2013), ARIA (Pace et al. 2014), DIRT (Das et al. , 2015), and RhizoVision Analyzer (Seethepalli, Anand et…”
Breeding for increased nitrogen‐use efficiency: a review for wheat (T. aestivum L.) (2016, background): “At an intermediate scale, the use of soilfilled root observation chambers (rhizotrons or clear-pot) (e.g. Lobet et al. 2011, Nagel et al. 2012, Richard et al. 2015) and non-destructive digital imaging techniques offers some promises (Manschadi et al. 2006, 2010), as X-ray computed tomography…”
In Situ Root System Architecture Extraction from Magnetic Resonance Imaging for Water Uptake Modeling (2013, methodology): “Detailed explanations on how SmartRoot works can be found in Lobet et al. (2011).”
GiA Roots: software for the high throughput analysis of plant root system architecture (2012, unknown): “In practice, end-users often utilize point-and-click approaches that are not scaleable to large numbers of edges within a root system (for an exception, see the recently released software “SmartRoot” which is designed for semi-automated analysis of the hierarchical structure of root systems [39]).”