Combining image analyses tools for comprehensive characterization of root systems from soil-filled rhizobox phenotyping platforms
More details
Hide details
Department of Crop Sciences, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Strasse 24, 3430 Tulln, Austria
Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Gregor-Mendel-Straße 33, 1180 Vienna, Austria
Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
Institute for Sustainable Agro-ecosystem Services, University of Tokyo, 1 Chome-1-1 Midoricho, Nishitokyo, Tokyo 188-0002, Japa
Gernot Bodner   

Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
Final revision date: 2021-10-06
Acceptance date: 2021-10-15
Publication date: 2021-11-10
Int. Agrophys. 2021, 35(3): 257–268
  • 1. Imaging visible length of roots at the surface of soil-filled rhizoboxes can predict total length.
  • 2. Different image analysis tools result in similar root length estimates despite specific segmentation approaches and provide inter-comparable root architecture descriptors.
  • 3. Structural equation modelling reveals that combining root size and branching traits can predict plant transpiration.
Root traits are fundamental for the resilience of plants under stress. Image-based phenotyping can provide relevant datasets to reveal the underlying root traits. However, root phenotyping is still hampered by methodological constrains, in particular the extraction of root traits from images taken under semi-natural conditions. In this study, we thus propose a strategy for analysing root images from rhizoboxes. Utilizing three Vicia faba genotypes and two soil moisture conditions, we applied software tools featuring distinctive types of root descriptors. We determined their accuracy in terms of root length measurement, inference from surface-visible root axes with regard to total root length, inter-relation between root architectural descriptors and their relevance to plant transpiration. Our results show that different image analysis tools provide similar root length estimates despite specific segmentation approaches. Several root architectural descriptors are also inter-comparable. Using structural equation modelling, we identified the relevant phenotyping root traits thereby characterizing root size and branching which –drives plant transpiration. We conclude that rhizobox systems are a promising platform for root phenotyping. Future developments in image analysis should overcome the requirement for manual post-processing (e.g. gap closure) and automate root architecture measurement thereby improving throughput and thus the range of rhizobox phenotyping applicability for plant breeding.
Belachew K.Y., Nagel K.A., Fiorani F., and Stoddard F.L., 2018. Diversity in root growth responses to moisture deficit in young faba bean (Vicia faba L.) plants. PeerJ 6, e4401.
Bodner G., Alsalem M., Nakhforoosh A., Arnold T., and Leitner D., 2017. RGB and spectral root imaging for plant phenotyping and physiological research: Experimental setup and imaging protocols. JoVE (J. Visualized Experiments), e56251.
Bodner G., Loiskandl W., Hartl W., Erhart E., and Sobotik M., 2019. Characterization of cover crop rooting types from integration of rhizobox imaging and root atlas information. Plants, 8, 514.
Bodner G., Nakhforoosh A., Arnold T., and Leitner D., 2018. Hyperspectral imaging: a novel approach for plant root phenotyping. Plant Methods, 14, 84.
Bontpart T., Concha C., Giuffrida M.V., Robertson I., Admkie K., Degefu T., Girma N., Tesfaye K., Haileselassie T., and Fikre A., 2020. Affordable and robust phenotyping framework to analyse root system architecture of soil‐grown plants. Plant J., 103, 2330-2343.
Chen H., Valerio Giuffrida M., Doerner P., and Tsaftaris S.A., 2019. Adversarial large-scale root gap inpainting. Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, pp. 0-0.
Chen Y., Ghanem M.E., and Siddique K.H.M., 2017. Characterising root trait variability in chickpea (Cicer arietinum L.) germplasm. J. Exp. Botany, 68, 1987-1999.
Chen Y., Rengel Z., Palta J., and Siddique K.H.M., 2018. Efficient root systems for enhancing tolerance of crops to water and phosphorus limitation. Indian J. Plant Physiol., 1-8.
Collins N.C., Tardieu F., and Tuberosa, R., 2008. Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiology, 147, 469-486.
Delory B.M., Weidlich E.W.A., Meder L., Lütje A., van Duijnen R., Weidlich R., and Temperton V.M., 2017. Accuracy and bias of methods used for root length measurements in functional root research. Methods Ecol. Evolution, 8, 1594-1606.
Downie H.F., Adu M.O., Schmidt S., Otten W., Dupuy L.X., White P.J., and Valentine T.A., 2015. Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis. Plant, Cell Environ., 38, 1213-1232.
Dubrovsky J.G., Soukup A., Napsucialy-Mendivil S., Jeknić Z., and Ivanchenko M.G., 2009. The lateral root initiation index: an integrative measure of primordium formation. Annals Botany, 103, 807-817.
Fitter A.H., 1987. An architectural approach to the comparative ecology of plant root systems. New Phytologist, 106, 61-77.
Freschet G.T., Roumet C., Comas L.H., Weemstra M., Bengough A.G., Rewald B., Bardgett R.D., De Deyn G.B., Johnson D., and Klimešová J., 2021. Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. New Phytologist, 232, 1123-1158.
Foxx A.J. and Fort F., 2019. Root and shoot competition lead to contrasting competitive outcomes under water stress: A systematic review and meta-analysis. PloS one, 14, e0220674.
Gioia T., Galinski A., Lenz H., Müller C., Lentz J., Heinz K., Briese C., Putz A., Fiorani F., and Watt M., 2017. GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply. Functional Plant Biol., 44, 76-93.
Haling R.E., Yang Z., Shadwell N., Culvenor R.A., Stefanski A., Ryan M.H., Sandral G.A., Kidd D.R., Lambers H., and Simpson R.J., 2016. Root morphological traits that determine phosphorus-acquisition efficiency and critical external phosphorus requirement in pasture species. Functional Plant Biol., 43, 815-826.
Himmelbauer M.L., Loiskandl W., and Kastanek F., 2004. Estimating length, average diameter and surface area of roots using two different image analyses systems. Plant and Soil, 260, 111-120.
Hodge A., Berta G., Doussan C., Merchan F., and Crespi M., 2009. Plant root growth, architecture and function. Plant Soil, 321, 153-187.
Johnson M.G., Tingey D.T., Phillips D.L., and Storm M.J., 2001. Advancing fine root research with minirhizotrons. Environ. Exp. Botany, 45, 263-289.
Kashiwagi J., Krishnamurthy L., Crouch J.H., and Serraj R., 2006. Variability of root length density and its contributions to seed yield in chickpea (Cicer arietinum L.) under terminal drought stress. Field Crops Res., 95, 171-181.
Kimura K., Kikuchi S., and Yamasaki Si., 1999. Accurate root length measurement by image analysis. Plant Soil, 216, 117-127.
Leitner D., Felderer B., Vontobel P., and Schnepf A., 2014. Recovering root system traits using image analysis exemplified by two-dimensional neutron radiography images of lupine. Plant Physiol., 164, 24-35.
Lobet G., 2017. Image analysis in plant sciences: publish then perish. Trends Plant Sci., 22, 559-566.
Lynch J.P., 2011. Root phenes for enhanced soil exploration and phosphorus acquisition: tools for future crops. Plant Physiol., 156, 1041-1049.
Manschadi A.M., Christopher J., and Hammer G.L., 2006. The role of root architectural traits in adaptation of wheat to water-limited environments. Functional Plant Biol., 33, 823-837.
Minervini M., Scharr H., and Tsaftaris S.A., 2015. Image analysis: the new bottleneck in plant phenotyping [applications corner]. IEEE Signal Processing Magazine, 32, 126-131.
Nagel K.A., Putz A., Gilmer F., Heinz K., Fischbach A., Pfeifer J., Faget M., Blossfeld S., Ernst M., and Dimaki C., 2012. GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Functional Plant Biol., 39, 891-904.
Pang J., Ryan M.H., Lambers H., and Siddique K.H.M., 2018. Phosphorus acquisition and utilisation in crop legumes under global change. Current Opinion Plant Biol., 45, 248-254.
Pfeifer J., Faget M., Walter A., Blossfeld S., Fiorani F., Schurr U., and Nagel K.A., 2014. Spring barley shows dynamic compensatory root and shoot growth responses when exposed to localised soil compaction and fertilisation. Functional Plant Biol., 41, 581-597.
Pierret A., Gonkhamdee S., Jourdan C., and Maeght J.-L., 2013. IJ_Rhizo: an open-source software to measure scanned images of root samples. Plant Soil, 373, 531-539.
Pound M.P., French A.P., Atkinson J.A., Wells D.M., Bennett M.J., and Pridmore T., 2013. RootNav: navigating images of complex root architectures. Plant Physiol., 162, 1802-1814.
Rose L. and Lobet G., 2019. Accuracy of image analysis tools for functional root traits: A comment on Delory et al. (2017). Methods Ecol. Evolution, 10(5), 702-711.
Schermelleh-Engel K., Moosbrugger H., and Müller H., 2003. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research online, 8, 23-74.
Trachsel S., Kaeppler S.M., Brown K.M., and Lynch J.P., 2013. Maize root growth angles become steeper under low N conditions. Field Crops Res., 140, 18-31.
Vadez V., 2014. Root hydraulics: the forgotten side of roots in drought adaptation. Field Crops Res., 165, 15-24.
Wagner S., Hoefer C., Prohaska T., and Santner J., 2020. Two-dimensional visualization and quantification of labile, inorganic plant nutrients and contaminants in soil. J. Vis. Exp., 163, e61661.
Wang T., Rostamza M., Song Z., Wang L., McNickle G., Iyer-Pascuzzi A.S., Qiu Z., and Jin J., 2019. SegRoot: a high throughput segmentation method for root image analysis. Computers Electronics Agric., 162, 845-854.
Watt M., Moosavi S., Cunningham S.C., Kirkegaard J.A., Rebetzke G.J., and Richards R.A., 2013. A rapid, controlled-environment seedling root screen for wheat correlates well with rooting depths at vegetative, but not reproductive, stages at two field sites. Annals Botany, 112, 447-455.
Zhao J., Bodner G., Rewald B., Leitner D., Nagel K.A., and Nakhforoosh A., 2017. Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems. J. Exp. Botany, 68, 965-982.