The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
RootNav: Navigating Images of Complex Root Architectures
Open accessAbstract
We present a novel image analysis tool that allows the semiautomated quantification of complex root system architectures in a range of plant species grown and imaged in a variety of ways. The automatic component of RootNav takes a top-down approach, utilizing the powerful expectation maximization classification algorithm to examine regions of the input image, calculating the likelihood that given pixels correspond to roots. This information is used as the basis for an optimization approach to root detection and quantification, which effectively fits a root model to the image data. The resulting user experience is akin to defining routes on a motorist’s satellite navigation system: RootNav makes an initial optimized estimate of paths from the seed point to root apices, and the user is able to easily and intuitively refine the results using a visual approach. The proposed method is evaluated on winter wheat (Triticum aestivum) images (and demonstrated on Arabidopsis [Arabidopsis thaliana], Brassica napus, and rice [Oryza sativa]), and results are compared with manual analysis. Four exemplar traits are calculated and show clear illustrative differences between some of the wheat accessions. RootNav, however, provides the structural information needed to support extraction of a wider variety of biologically relevant measures. A separate viewer tool is provided to recover a rich set of architectural traits from RootNav’s core representation.
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Cites
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Cited by
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix083 · Cited by 389
Self-citationContrasting cadmium resistance strategies in two metallicolous populations of Arabidopsis halleri
Corso, M., Schvartzman, M. S., Guzzo, F., Souard, F., Malkowski, E., Hanikenne, M., & Verbruggen, N. (2018) · New Phytologist · doi:10.1111/nph.14948 · Cited by 110
Svobodníková, L., Kummerová, M., Zezulka, Š., Babula, P., & Sendecká, K. (2020) · Chemosphere · doi:10.1016/j.chemosphere.2020.127411 · Cited by 31
Guo, Q., Ng, P. Q., Shi, S., Fan, D., Li, J., Zhao, J., Wang, H., David, R., Mittal, P., Do, T., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0225064 · Cited by 25
Metadata
- Authors
- Pound, M. P., French, A. P., Atkinson, J. A., Wells, D. M., Bennett, M. J., & Pridmore, T.
- Venue
- Plant Physiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-13
- Volume
- 162
- Issue
- 4
- Pages
- 1802-1814
- ISSN
- 0032-0889 (ISSN-L)
- 1532-2548
- OpenAlex
- W2163834574
- Cited by
- 266
- References
- 23
- FWCI
- 15.71
- Type
- journal article
- Language
- en
- Open access
- green
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- Pound, M. P., French, A. P., Atkinson, J. A., Wells, D. M., Bennett, M. J., & Pridmore, T. (2013). RootNav: Navigating Images of Complex Root Architectures. Plant Physiology. https://doi.org/10.1104/pp.113.221531
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