Combining image analyses tools for comprehensive characterization of root systems from soil-filled rhizobox phenotyping platforms

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.

Automated summary

Root traits are crucial for plant resilience under stress. Image-based phenotyping can reveal relevant datasets on root traits, but faces challenges in methodology. This study proposes a strategy for analyzing root images and identifies inter-related root descriptors.