Bird, Jordan J., Barnes, Chloe M., Manso, Luis J., Ekárt, Anikó and Faria, Diego R. (2022). Fruit quality and defect image classification with conditional GAN data augmentation. Scientia Horticulturae, 293 ,
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 (
| Publication DOI: | https://doi.org/10.1016/j.scienta.2021.110684 |
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| Divisions: | ?? 50811700Jl ?? College of Engineering & Physical Sciences College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE) College of Engineering & Physical Sciences > Systems analytics research institute (SARI) College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies |
| Publication ISSN: | 0304-4238 |
| Full Text Link: | |
| 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.
(
0000-0002-9858-1231)
Barnes, Chloe M. (
0000-0002-6782-1773)
Manso, Luis J. (
0000-0003-2616-1120)
Ekárt, Anikó (
0000-0001-6967-5397)
Faria, Diego R. (
0000-0002-2771-1713)
|
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CORE (COnnecting REpositories)