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dc.contributor.author Awty-Carroll, Danny
dc.contributor.author Clifton-Brown, John
dc.contributor.author Robson, Paul
dc.date.accessioned 2018-06-11T18:41:51Z
dc.date.available 2018-06-11T18:41:51Z
dc.date.issued 2018-01-17
dc.identifier.citation Awty-Carroll , D , Clifton-Brown , J & Robson , P 2018 , ' Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis ' Plant Methods , vol. 14 , 5 . https://doi.org/10.1186/s13007-018-0272-0 en
dc.identifier.issn 1746-4811
dc.identifier.other PURE: 18969296
dc.identifier.other PURE UUID: 06437bc8-de0e-4e68-b268-27afe5a36b6a
dc.identifier.other Scopus: 85040688883
dc.identifier.other PubMed: 29371877
dc.identifier.other PubMedCentral: PMC5771004
dc.identifier.other handle.net: 2160/46599
dc.identifier.other ORCID: /0000-0001-5855-0775/work/51727637
dc.identifier.other ORCID: /0000-0003-1841-3594/work/61776319
dc.identifier.uri http://hdl.handle.net/2160/46599
dc.description.abstract Background: Miscanthus is a leading second generation bio-energy crop, which is currently planted using rhizomes; however, increasingly the use of seed is being explored to improve efficiency of propagation. Miscanthus seed are small, germination is often poor and without sterilisation so germination detection must be sufficiently adaptable for example to the presence or absence of mould. Results: Machine learning using k-NN improved the scoring of different seed phenotypes encountered in scoring germination for Miscanthus. The k-NN-based algorithm was effective in scoring the germination of seed images when compared with human scores of the same images. The trueness of the k-NN result was 0.69 to 0.7, as measured using the area under a ROC curve. When the k-NN classifier was tested on an optimised image subset of seed an area under the ROC curve of 0.89 was achieved. Conclusions: With non-ideal seed images that included mould and broken seed the k-NN classifier was less consistent with human assessments. The most accurate assessment of germination with which to train classifiers is difficult to determine but the k-NN classifier provided an impartial consistent measurement of this important trait. It was more reproducible than the existing human scoring methods and was demonstrated to give a high degree of trueness to the human score. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof Plant Methods en
dc.rights en
dc.subject germination en
dc.subject k-NN en
dc.subject machine learning en
dc.subject Miscanthus en
dc.subject seed en
dc.subject Image analysis en
dc.subject Bioenergy en
dc.subject seed imaging en
dc.subject robust classification en
dc.title Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version publishersversion en
dc.description.version publishersversion en
dc.identifier.doi https://doi.org/10.1186/s13007-018-0272-0
dc.contributor.institution Department of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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