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      TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

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          Abstract

          Background

          3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture.

          Results

          We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image.

          Conclusions

          TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13007-021-00829-z.

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          Most cited references28

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          Root Architecture and Plant Productivity.

          J. Lynch (1995)
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            Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field

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              Root system architecture: opportunities and constraints for genetic improvement of crops.

              Abiotic stresses increasingly curtail crop yield as a result of global climate change and scarcity of water and nutrients. One way to minimize the negative impact of these factors on yield is to manipulate root system architecture (RSA) towards a distribution of roots in the soil that optimizes water and nutrient uptake. It is now established that most of the genetic variation for RSA is driven by a suite of quantitative trait loci. As we discuss here, marker-assisted selection and quantitative trait loci cloning for RSA are underway, exploiting genomic resources, candidate genes and the knowledge gained from Arabidopsis, rice and other crops. Nonetheless, efficient and accurate phenotyping, modelling and collaboration with breeders remain important challenges, particularly when defining ideal RSA for different crops and target environments.
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                Author and article information

                Contributors
                danzeng8@gmail.com
                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central (London )
                1746-4811
                13 December 2021
                13 December 2021
                2021
                : 17
                : 127
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Computer Science and Engineering, , Washington University in St. Louis, ; Saint Louis, MO 63130 USA
                [2 ]GRID grid.34424.35, ISNI 0000 0004 0466 6352, Donald Danforth Plant Science Center, ; Saint Louis, MO 63132 USA
                [3 ]GRID grid.262962.b, ISNI 0000 0004 1936 9342, Department of Computer Science, , Saint Louis University, ; Saint Louis, MO 63103 USA
                Author information
                http://orcid.org/0000-0002-2636-3729
                Article
                829
                10.1186/s13007-021-00829-z
                8667396
                34903248
                94661a43-b189-4521-aa39-999a8e104b7c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 24 August 2021
                : 30 November 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DBI-1759836
                Award ID: EF-1921728
                Award ID: DBI-1759807
                Award ID: CCF-1907612
                Award ID: CCF-2106672
                Award ID: DBI-1759796
                Award ID: IOS-1638507
                Award ID: DBI-1759807
                Award ID: DBI-1759836
                Award Recipient :
                Categories
                Methodology
                Custom metadata
                © The Author(s) 2021

                Plant science & Botany
                root system architecture,phenotyping,3d imaging,topology,computer graphics
                Plant science & Botany
                root system architecture, phenotyping, 3d imaging, topology, computer graphics

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