rhizoTrak: a flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons

  • Birgit MöllerEmail author
  • Hongmei ChenEmail author
  • Tino Schmidt
  • Axel Zieschank
  • Roman Patzak
  • Manfred Türke
  • Alexandra Weigelt
  • Stefan Posch
Methods Paper


Background and aims

Minirhizotrons are commonly used to study root turnover which is essential for understanding ecosystem carbon and nutrient cycling. Yet, extracting data from minirhizotron images requires extensive annotation effort. Existing annotation tools often lack flexibility and provide only a subset of the required functionality. To facilitate efficient root annotation in minirhizotrons, we present the user-friendly open source tool rhizoTrak.

Methods and results

rhizoTrak builds on TrakEM2 and is publicly available as Fiji plugin. It uses treelines to represent branching structures in roots and assigns customizable status labels per root segment. rhizoTrak offers configuration options for visualization and various functions for root annotation mostly accessible via keyboard shortcuts. rhizoTrak allows time-series data import and particularly supports easy handling and annotation of time-series images. This is facilitated via explicit temporal links (connectors) between roots which are automatically generated when copying annotations from one image to the next. rhizoTrak includes automatic consistency checks and guided procedures for resolving inconsistencies. It facilitates easy data exchange with other software by supporting open data formats.


rhizoTrak covers the full range of functions required for user-friendly and efficient annotation of time-series images. Its flexibility and open source nature will foster efficient data acquisition procedures in root studies using minirhizotrons.


minirhizotron images Time-series Manual annotation Open source Free Platform-independent 



We are grateful to Nico Eisenhauer and Georg Mathias for their support to develop this software via the iDiv Ecotron platform. In addition, we thank Berit Schreck for her support in software development and testing, and Lisa Ertel for experimentally evaluating rhizoTrak.


H. Chen was funded by the German Science Foundation (DFG, Jena Experiment research group FOR 1451). M. Türke, T. Schmidt and A. Zieschank were co-funded by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118) and by the Helmholtz Association in the framework of the iDiv Ecotron research platform.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Supplementary material

11104_2019_4199_MOESM1_ESM.pdf (1.7 mb)
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11104_2019_4199_MOESM2_ESM.csv (521 kb)
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11104_2019_4199_MOESM3_ESM.csv (2 kb)
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11104_2019_4199_MOESM4_ESM.pdf (56 kb)
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  1. Cardona A, Saalfeld S, Schindelin J, Arganda-Carreras I, et al. (2012) TrakEM2 software for neural circuit reconstruction. PloS ONE 7(6):e38011CrossRefGoogle Scholar
  2. Colombi T, Kirchgessner N, Marié L, Andrée C, et al. (2015) Next generation shovelomics: set up a tent and REST. Plant Soil 388(1–2):1–20CrossRefGoogle Scholar
  3. de Kroon H, Visser E (eds) (2003) Root ecology, ecological studies, vol 168. Springer, BerlinGoogle Scholar
  4. Erz G, Veste M, Anlauf H, Breckle SW, Posch S (2005) A region and contour based technique for automatic detection of tomatoe roots in minirhizotron images. J Appl Botany Food Qual 79:83–88Google Scholar
  5. Gregory PJ (2008) Plant roots: growth, activity and interactions with the soil. WileyGoogle Scholar
  6. Hendrick RL, Pregitzer KS (1992) The demography of fine roots in a northern hardwood forest. Ecology 73(3):1094–1104CrossRefGoogle Scholar
  7. Jackson RB, Mooney HA, Schulze ED (1997) A global budget for fine root biomass, surface area, and nutrient contents. Proc Natl Acad Sci 94(14):7362–7366.
  8. Johnson MG, Tingey DT, Phillips DL, Storm MJ (2001) Advancing fine root research with minirhizotrons. Environ and Exp Botany 45(3):263–289CrossRefGoogle Scholar
  9. Le Bot J, Serra V, Fabre J, Draye X, et al. (2010) DART: a software to analyse root system architecture and development from captured images. Plant Soil 326(1):261–273Google Scholar
  10. Lobet G, Pagès L, Draye X (2011) A novel image-analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiol 157(1):29–39CrossRefGoogle Scholar
  11. Lobet G, Pound MP, Diener J, Pradal C, et al. (2015) Root system markup language: toward a unified root architecture description language. Plant Physiol 167(3):617– 627CrossRefGoogle Scholar
  12. Lukac M (2012) Fine root turnover. In: Measuring roots. Springer, pp 363–373Google Scholar
  13. Majdi H (1996) Root sampling methods – applications and limitations of the minirhizotron technique. Plant Soil 185(2):255–258CrossRefGoogle Scholar
  14. Manuilova E, Schuetzenmeister A, Model F (2018) mcr: method comparison regression. R package, version 1.2.1, available via, Accessed 15 Jan 2019
  15. McCormack ML, Dickie IA, Eissenstat DM, Fahey TJ, et al. (2015) Redefining fine roots improves understanding of below-ground contributions to terrestrial biosphere processes. N Phytol 207 (3):505–518. CrossRefGoogle Scholar
  16. Nagel KA, Putz A, Gilmer F, Heinz K, 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(11):891–904CrossRefGoogle Scholar
  17. Passing H, Bablok W (1983) A new biometrical procedure for testing the equality of measurements from two different analytical methods. application of linear regression procedures for method comparison studies in clinical chemistry, part I. Clin Chem Lab Med 21(11):709–720CrossRefGoogle Scholar
  18. Poorter H, Niklas KJ, Reich PB, Oleksyn J, et al. (2012) Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. N Phytol 193(1):30–50CrossRefGoogle Scholar
  19. Pound MP, French AP, Atkinson JA, Wells DM, et al. (2013) RootNav: navigating images of complex root architectures. Plant Physiol 162(4):1802–1814CrossRefGoogle Scholar
  20. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, website, Accessed 15 Jan 2019
  21. Rellán-Álvarez R, Lobet G, Lindner H, Pradier PL, et al. (2015) GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems. eLife 4:e07597CrossRefGoogle Scholar
  22. Rewald B, Ephrath JE (2013) Minirhizotron techniques. Plant roots: The hidden half 42:1–15Google Scholar
  23. Rewald B, Meinen C, Trockenbrodt M, Ephrath JE, Rachmilevitch S (2012) Root taxa identification in plant mixtures – current techniques and future challenges. Plant Soil 359(1):165–182CrossRefGoogle Scholar
  24. RSML Github Repo (2018) RSML resources, accessed 15 Jan (2019),
  25. RSML Homepage (2015) RSML. Accessed 15 Jan (2019),
  26. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, et al. (2012) Fiji: an open-source platform for biological-image analysis. Nature Meth 9(7):676CrossRefGoogle Scholar
  27. Smit AL, Bengough AG, Engels C, van Noordwijk M et al (eds) (2000) Root methods: a handbook. Springer, BerlinGoogle Scholar
  28. Vamerali T, Bandiera M, Mosca G (2012) Minirhizotrons in modern root studies. Springer, Berlin, pp 341–361Google Scholar
  29. Weisser WW, Roscher C, Meyer ST, Ebeling A, et al. (2017) Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: patterns, mechanisms, and open questions. Basic and Appl Ecology 23:1–73CrossRefGoogle Scholar
  30. Zeng G, Birchfield S, Wells C (2010) Rapid automated detection of roots in minirhizotron images. Mach Vis and Appl 21(3):309–317CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer ScienceMartin Luther University Halle-WittenbergHalle (Saale)Germany
  2. 2.Institute of BiologyLeipzig UniversityLeipzigGermany
  3. 3.German Center of Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigLeipzigGermany
  4. 4.Institute of Biological and Medical ImagingHelmholtz Zentrum München - German Research Center for Environmental HealthNeuherbergGermany

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