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<https://opendata.unex.es/recurso/ciencia-tecnologia/investigacion/publicaciones/Publicacion/2022-116> fabio:hasPublicationYear "2022";
    bibo:volume "293";
    bibo:doi "10.1016/j.scienta.2021.110684";
    dcterms:contributor "Jordan J. Bird; Chloe Barnes; Luis J. Manso; Aniko Ekart; D. R. Faria";
    vivo:identifier "2022-116";
    bibo:issn "0304-4238";
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    dcterms:publisher "Scientia Horticulturae";
    dcterms:created "2022-02-05T00:00:00"^^xsd:dateTime;
    dcterms:creator "Bird J.J.";
    ou:bibtex "@article{0de7fec0032e4586ab05ecf244ba97ef, title = 'Fruit quality and defect image classification with conditional GAN data augmentation', 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 (', author = 'Bird, {Jordan J.} and Barnes, {Chloe M.} and Manso, {Luis J.} and Anik{\\'o} Ek{\\'a}rt and Faria, {Diego R.}', year = '2022', month = feb, day = '5', doi = '10.1016/j.scienta.2021.110684', language = 'English', volume = '293', journal = 'Scientia Horticulturae', issn = '0304-4238', publisher = 'Elsevier', }";
    dcterms:title "Fruit quality and defect image classification with conditional GAN data augmentation";
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