TY - JOUR
T1 - Fruit quality and defect image classification with conditional GAN data augmentation
AU - Bird, Jordan J.
AU - Barnes, Chloe M.
AU - Manso, Luis J.
AU - Ekárt, Anikó
AU - Faria, Diego R.
PY - 2022/2/5
Y1 - 2022/2/5
N2 - 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 (
AB - 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 (
UR - https://www.sciencedirect.com/science/article/pii/S0304423821007913?via%3Dihub
U2 - 10.1016/j.scienta.2021.110684
DO - 10.1016/j.scienta.2021.110684
M3 - Article
SN - 0304-4238
VL - 293
JO - Scientia Horticulturae
JF - Scientia Horticulturae
M1 - 110684
ER -