GT-RootS: An integrated software for automated root system measurement from high-throughput phenotyping platform images

Abstract : GT-RootS (Global Traits of Root System) is an automated Java-based open-source solution we are developing for processing root system images provided by the Rhizoscope, a CIRAD phenotyping platform dedicated to dense cereal plants. Two types of use are proposed. The fully-automated mode applies a predefined standard processing pipeline to a preselected set of images while the semi-automated mode allows the user to interactively check and correct intermediate processing results to a specific image. In both cases, GT-RootS combines a local adaptive thresholding algorithm and a similarity indicator to automatically separate the root system from a complex background without user intervention. A covering house-shaped polygon is then defined in the axis system of the root ellipse from vertical weighted density profiles. This canonical shape is composed of both upper trapezoid and lower rectangular compartments from which upper and lower heights, global width and local offset, root system cone angulation and spatial densities can be easily evaluated and displayed. GT-RootS measurements were compared both to expert evaluations and to two other estimation methods on a set of 64 images of a dense Japonica rice root system of 30-days-old plants. We demonstrate also that GT-RootS satisfies the requirements of high-throughput analyses: short processing time (around 30 images per hour on a low-end computer), measurement accuracy and repeatability, and user bias eradication.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01790777
Contributor : Gérard Subsol <>
Submitted on : Monday, May 14, 2018 - 8:43:30 AM
Last modification on : Monday, November 18, 2019 - 3:44:44 PM

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Philippe Borianne, Gérard Subsol, Franz Fallavier, Audrey Dardou, Alain Audebert. GT-RootS: An integrated software for automated root system measurement from high-throughput phenotyping platform images. Computers and Electronics in Agriculture, Elsevier, 2018, 150, pp.328-342. ⟨10.1016/j.compag.2018.05.003⟩. ⟨lirmm-01790777⟩

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