Combining image analyses tools for comprehensive characterization of root systems from soil-filled rhizobox phenotyping platforms
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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
Final revision date: 2021-10-06
Acceptance date: 2021-10-15
Publication date: 2021-11-10
Corresponding author
Gernot Bodner   

Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
Int. Agrophys. 2021, 35(3): 257-268
  • Imaging visible length of roots at the surface of soil-filled rhizoboxes can predict total length.
  • Different image analysis tools result in similar root length estimates despite specific segmentation approaches and provide inter-comparable root architecture descriptors.
  • 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.
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