archiDART: an R package for the automated computation of plant root architectural traits

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

  • Benjamin M. Delory
  • Caroline Baudson
  • Yves Brostaux
  • Guillaume Lobet
  • Patrick du Jardin
  • Loïc Pagès
  • Pierre Delaplace

Background and aims: In order to analyse root system architectures (RSAs) from captured images, a variety of manual (e.g. Data Analysis of Root Tracings, DART), semi-automated and fully automated software packages have been developed. These tools offer complementary approaches to study RSAs and the use of the Root System Markup Language (RSML) to store RSA data makes the comparison of measurements obtained with different (semi-) automated root imaging platforms easier. The throughput of the data analysis process using exported RSA data, however, should benefit greatly from batch analysis in a generic data analysis environment (R software). 

Methods: We developed an R package (archiDART) with five functions. It computes global RSA traits, root growth rates, root growth directions and trajectories, and lateral root distribution from DART-generated and/or RSML files. It also has specific plotting functions designed to visualise the dynamics of root system growth. 

Results: The results demonstrated the ability of the package’s functions to compute relevant traits for three contrasted RSAs (Brachypodium distachyon [L.] P. Beauv., Hevea brasiliensis Müll. Arg. and Solanum lycopersicum L.). 

Conclusions: This work extends the DART software package and other image analysis tools supporting the RSML format, enabling users to easily calculate a number of RSA traits in a generic data analysis environment.

Original languageEnglish
JournalPlant and Soil
Volume398
Issue number1-2
Pages (from-to)351-365
Number of pages15
ISSN0032-079X
DOIs
Publication statusPublished - 01.2016

    Research areas

  • Plant root system architecture, Data Analysis of Root Tracings (DART), Root System Markup Language (RSML), 2D dynamic analysis, Root trait
  • Biology
  • Sustainability Science

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