Fruit quality and defect image classification with conditional GAN data augmentation


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 (

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
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College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Publication ISSN: 0304-4238
Last Modified: 29 May 2024 07:20
Date Deposited: 04 Nov 2021 13:42
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Related URLs: https://www.sci ... 7913?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2022-02-05
Published Online Date: 2021-11-01
Accepted Date: 2021-10-18
Authors: Bird, Jordan J. (ORCID Profile 0000-0002-9858-1231)
Barnes, Chloe M. (ORCID Profile 0000-0002-6782-1773)
Manso, Luis J. (ORCID Profile 0000-0003-2616-1120)
Ekárt, Anikó (ORCID Profile 0000-0001-6967-5397)
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)



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