Plant and Soil

, Volume 398, Issue 1–2, pp 351–365 | Cite as

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

  • Benjamin M. Delory
  • Caroline Baudson
  • Yves Brostaux
  • Guillaume Lobet
  • Patrick du Jardin
  • Loïc Pagès
  • Pierre DelaplaceEmail author
Regular Article


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).


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.


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.).


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.


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



Delory B.M. (Research Fellow) and Lobet G. (Postdoctoral Researcher) are financially supported by the Belgian National Fund for Scientific Research. The authors would like to thank Jacques Le Bot (INRA, Centre PACA, UR 1115 PSH) for providing the vectorized root system of the tomato plant used in this paper, and Pierre Tocquin (University of Liège, PhytoSYSTEMS) and three anonymous reviewers for their helpful comments on the manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Bucksch A, Burridge J, York LM et al (2014) Image-based high-throughput field phenotyping of crop roots. Plant Physiol 166:470–486. doi: 10.1104/pp. 114.243519 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Cai J, Zeng Z, Connor JN et al (2015) RootGraph : a graphic optimization tool for automated image analysis of plant roots. J Exp Bot. doi: 10.1093/jxb/erv359 Google Scholar
  3. Clark RT, MacCurdy RB, Jung JK et al (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiol 156:455–465. doi: 10.1104/pp. 110.169102 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Clark RT, Famoso AN, Zhao K et al (2013) High-throughput two-dimensional root system phenotyping platform facilitates genetic analysis of root growth and development. Plant Cell Environ 36:454–466. doi: 10.1111/j.1365-3040.2012.02587.x CrossRefPubMedGoogle Scholar
  5. Cobb JN, DeClerck G, Greenberg A et al (2013) Next-generation phenotyping : requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126:867–887. doi: 10.1007/s00122-013-2066-0 CrossRefPubMedPubMedCentralGoogle Scholar
  6. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL
  7. De Dorlodot S, Forster B, Pagès L et al (2007) Root system architecture: opportunities and constraints for genetic improvement of crops. Trends Plant Sci 12:474–481. doi: 10.1016/j.tplants.2007.08.012 CrossRefPubMedGoogle Scholar
  8. De Kroon H (2007) How do roots interact? Science 318:1562–1563. doi: 10.1126/science.1150726 CrossRefPubMedGoogle Scholar
  9. Delaplace P, Delory BM, Baudson C et al (2015) Influence of rhizobacterial volatiles on the root system architecture and the production and allocation of biomass in the model grass Brachypodium distachyon (L.) P. Beauv. BMC Plant Biol 15:1–15. doi: 10.1186/s12870-015-0585-3 CrossRefGoogle Scholar
  10. Delory BM, Baudson C, Brostaux Y et al (2015) archiDART: plant root system architecture analysis using DART and RSML files. R package version 1.1.
  11. Den Herder G, Van Isterdael G, Beeckman T, De Smet I (2010) The roots of a new green revolution. Trends Plant Sci 15:600–607. doi: 10.1016/j.tplants.2010.08.009 CrossRefGoogle Scholar
  12. Diener J, Nacry P, Périn C et al (2013) An automated image-processing pipeline for high-throughput analysis of root architecture in OpenAlea. 7th Int. Conf. Funct. Plant Model. Saariselkä, Finland, pp 85–87Google Scholar
  13. Dupuy L, Gregory PJ, Glyn Bengough A (2010) Root growth models: towards a new generation of continuous approaches. J Exp Bot 61:2131–2143. doi: 10.1093/jxb/erp389 CrossRefPubMedGoogle Scholar
  14. Faget M, Nagel KA, Walter A et al (2013) Root-root interactions: extending our perspective to be more inclusive of the range of theories in ecology and agriculture using in-vivo analyses. Ann Bot 112:253–266. doi: 10.1093/aob/mcs296 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:267–291. doi: 10.1146/annurev-arplant-050312-120137 CrossRefPubMedGoogle Scholar
  16. Forde B, Lorenzo H (2001) The nutritional control of root development. Plant Soil 232:51–68. doi: 10.1023/A:1010329902165 CrossRefGoogle Scholar
  17. French A, Ubeda-Tomás S, Holman TJ et al (2009) High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiol 150:1784–1795. doi: 10.1104/pp. 109.140558 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Giehl RFH, Gruber BD, von Wirén N (2014) It’s time to make changes: modulation of root system architecture by nutrient signals. J Exp Bot 65:769–778. doi: 10.1093/jxb/ert421 CrossRefPubMedGoogle Scholar
  19. Godin C, Sinoquet H (2005) Functional – structural plant modelling. New Phytol 166:705–708CrossRefPubMedGoogle Scholar
  20. Gonkhamdee S, Pierret A, Maeght J-L et al (2010) Effects of corn (Zea mays L.) on the local and overall root development of young rubber tree (Hevea brasiliensis Muel. Arg). Plant Soil 334:335–351. doi: 10.1007/s11104-010-0386-2 CrossRefGoogle Scholar
  21. Iyer-Pascuzzi AS, Symonova O, Mileyko Y et al (2010) Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiol 152:1148–1157. doi: 10.1104/pp. 109.150748 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Kumar P, Huang C, Cai J, Miklavcic SJ (2014) Root phenotyping by root tip detection and classification through statistical learning. Plant Soil 380:193–209. doi: 10.1007/s11104-014-2071-3 CrossRefGoogle Scholar
  23. Le Bot J, Serra V, Fabre J et al (2010) DART: a software to analyse root system architecture and development from captured images. Plant Soil 326:261–273. doi: 10.1007/s11104-009-0005-2 CrossRefGoogle Scholar
  24. Le Marié C, Kirchgessner N, Marschall D et al (2014) Rhizoslides : paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis. Plant Methods 10:1–16. doi: 10.1186/1746-4811-10-13 CrossRefGoogle Scholar
  25. Le Roux Y, Pagès L (1994) Développement et polymorphisme racinaires chez de jeunes semis d’hévéa (Hevea brasiliensis). Can J Bot 72:924–932CrossRefGoogle Scholar
  26. Leitner D, Felderer B, Vontobel P, Schnepf A (2014) Recovering root system traits using image analysis exemplified by two-dimensional neutron radiography images of lupine. Plant Physiol 164:24–35. doi: 10.1104/pp. 113.227892 CrossRefPubMedGoogle Scholar
  27. Lobet G (2015) rsml: Plant Root System Markup Language (RSML) file processing. R package version 1.2.
  28. Lobet G, Pagès L, Draye X (2011) A novel image-analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiol 157:29–39. doi: 10.1104/pp. 111.179895 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Lobet G, Draye X, Périlleux C (2013) An online database for plant image analysis software tools. Plant Methods 9:1–7. doi: 10.1186/1746-4811-9-38 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Lobet G, Pound MP, Diener J et al (2015) Root System Markup Language : toward an unified root architecture description language. Plant Physiol 167:617–627. doi: 10.1104/pp. 114.253625 CrossRefPubMedPubMedCentralGoogle Scholar
  31. López-Bucio J, Cruz-Ramírez A, Herrera-Estrella L (2003) The role of nutrient availability in regulating root architecture. Curr Opin Plant Biol 6:280–287. doi: 10.1016/S1369-5266(03)00035-9 CrossRefPubMedGoogle Scholar
  32. Lynch J (1995) Root architecture and plant productivity. Plant Physiol 109:7–13CrossRefPubMedPubMedCentralGoogle Scholar
  33. Lynch JP (2013) Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann Bot 112:347–357. doi: 10.1093/aob/mcs293 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Malamy JE (2005) Intrinsic and environmental response pathways that regulate root system architecture. Plant Cell Environ 28:67–77CrossRefPubMedGoogle Scholar
  35. Mathieu L, Lobet G, Tocquin P, Périlleux C (2015) “Rhizoponics”: a novel hydroponic rhizotron for root system analyses on mature Arabidopsis thaliana plants. Plant Methods 11:1–7. doi: 10.1186/s13007-015-0046-x CrossRefGoogle Scholar
  36. Meister R, Rajani MS, Ruzicka D, Schachtman DP (2014) Challenges of modifying root traits in crops for agriculture. Trends Plant Sci 19:779–788. doi: 10.1016/j.tplants.2014.08.005 CrossRefPubMedGoogle Scholar
  37. Nagel KA, Putz A, Gilmer F et al (2012) GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Funct Plant Biol 39:891–904CrossRefGoogle Scholar
  38. Pace J, Lee N, Naik HS et al (2014) Analysis of maize (Zea mays L.) seedling roots with the high-throughput image analysis tool ARIA (Automatic Root Image Analysis). PLoS One 9, e108255. doi: 10.1371/journal.pone.0108255 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Pagès L (2014) Branching patterns of root systems: quantitative analysis of the diversity among dicotyledonous species. Ann Bot 114:591–598. doi: 10.1093/aob/mcu145 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Pagès L, Bécel C, Boukcim H et al (2013) Calibration and evaluation of ArchiSimple, a simple model of root system architecture. Ecol Model 290:76–84. doi: 10.1016/j.ecolmodel.2013.11.014 CrossRefGoogle Scholar
  41. Pound MP, French AP, Atkinson JA et al (2013) RootNav: navigating images of complex root architectures. Plant Physiol 162:1802–1814. doi: 10.1104/pp. 113.221531 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Rascher U, Blossfeld S, Fiorani F et al (2011) Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 38:968–983. doi: 10.1071/FP11164 CrossRefGoogle Scholar
  43. Rich SM, Watt M (2013) Soil conditions and cereal root system architecture: review and considerations for linking Darwin and Weaver. J Exp Bot 64:1193–1208. doi: 10.1093/jxb/ert043 CrossRefPubMedGoogle Scholar
  44. Schmid C, Bauer S, Bartelheimer M (2015) Should I stay or should I go? Roots segregate in response to competition intensity. Plant Soil 391:283–291. doi: 10.1007/s11104-015-2419-3 CrossRefGoogle Scholar
  45. Slovak R, Göschl C, Su X et al (2014) A scalable open-source pipeline for large-scale root phenotyping of Arabidopsis. Plant Cell 26:2390–2403. doi: 10.1105/tpc.114.124032 CrossRefPubMedPubMedCentralGoogle Scholar
  46. Thaler P, Pagès L (1996a) Root apical diameter and root elongation rate of rubber seedlings (Hevea brasiliensis) show parallel responses to photoassimilate availability. Physiol Plant 97:365–371CrossRefGoogle Scholar
  47. Thaler P, Pagès L (1996b) Periodicity in the development of the root system of young rubber trees (Hevea brasiliensis Muell. Arg.): relationship with shoot development. Plant Cell Environ 19:56–64CrossRefGoogle Scholar
  48. Uga Y, Sugimoto K, Ogawa S et al (2013) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Genet 45:1097–1102. doi: 10.1038/ng.2725 CrossRefPubMedGoogle Scholar
  49. Wells DM, French AP, Naeem A et al (2012) Recovering the dynamics of root growth and development using novel image acquisition and analysis methods. Philos Trans R Soc B 367:1517–1524. doi: 10.1098/rstb.2011.0291 CrossRefGoogle Scholar
  50. Wu J, Pagès L, Wu Q et al (2014) Three-dimensional architecture of axile roots of field-grown maize. Plant Soil 387:363–377. doi: 10.1007/s11104-014-2307-2 CrossRefGoogle Scholar
  51. Zhu J, Ingram PA, Benfey PN, Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Curr Opin Plant Biol 14:310–317. doi: 10.1016/j.pbi.2011.03.020 CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benjamin M. Delory
    • 1
  • Caroline Baudson
    • 1
  • Yves Brostaux
    • 2
  • Guillaume Lobet
    • 3
  • Patrick du Jardin
    • 1
  • Loïc Pagès
    • 4
  • Pierre Delaplace
    • 1
    Email author
  1. 1.Plant BiologyUniversity of Liège – Gembloux Agro-Bio TechGemblouxBelgium
  2. 2.Applied Statistics, Computer Science and ModelingUniversity of Liège – Gembloux Agro-Bio TechGemblouxBelgium
  3. 3.Laboratory of Plant Physiology, PhytoSYSTEMSUniversity of LiègeLiègeBelgium
  4. 4.INRA, Centre PACA, UR 1115 PSH, Domaine Saint-Paul, Site AgroparcAvignonFrance

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