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<http://opendata.unex.es/recurso/ciencia-tecnologia/investigacion/publicaciones/Publicacion/2022-116> dcterms:publisher "Scientia Horticulturae";
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    ou:bibtex "@article{0de7fec0032e4586ab05ecf244ba97ef,\n  title     = 'Fruit quality and defect image classification with conditional GAN data augmentation',\n  abstract  = 'Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or damaged. State-of-the-art works in the field report high accuracy results on small datasets (',\n  author    = 'Bird, {Jordan J.} and Barnes, {Chloe M.} and Manso, {Luis J.} and Anik{\\'o} Ek{\\'a}rt and Faria, {Diego R.}',\n  year      = '2022',\n  month     = feb,\n  day       = '5',\n  doi       = '10.1016/j.scienta.2021.110684',\n  language  = 'English',\n  volume    = '293',\n  journal   = 'Scientia Horticulturae',\n  issn      = '0304-4238',\n  publisher = 'Elsevier',\n}";
    ou:vecesCitado "54";
    dcterms:creator "Bird J.J.";
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    bibo:volume "293";
    dcterms:title "Fruit quality and defect image classification with conditional GAN data augmentation";
    dcterms:contributor "Bird J.J., Barnes C.M., Manso L.J., Ekart A., Faria D.R.";
    ou:urlOrcid <https://research.aston.ac.uk/en/publications/0de7fec0-032e-4586-ab05-ecf244ba97ef>;
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    ou:publicadaEnRevista <http://opendata.unex.es/recurso/ciencia-tecnologia/investigacion/revistas/Revista/2022-2032>.
<http://opendata.unex.es/recurso/educacion/universidad/personal/0eabafa30f2481975f11f68ed070d4f7-luis-jesus-manso-fernandez-arguelles> ou:tienePublicacion <http://opendata.unex.es/recurso/ciencia-tecnologia/investigacion/publicaciones/Publicacion/2022-116>.