Fruit quality and defect image classification with conditional GAN data augmentation

Jordan J. Bird; Chloe M. Barnes; Luis J. Manso; Anikó Ekárt; Diego R. Faria

doi:10.1016/j.scienta.2021.110684

Fruit quality and defect image classification with conditional GAN data augmentation

Jordan J. Bird^*, Chloe M. Barnes, Luis J. Manso, Anikó Ekárt, Diego R. Faria

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

119 Citations (SciVal)

24 Downloads (Pure)

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 (

Original language	English
Article number	110684
Journal	Scientia Horticulturae
Volume	293
Early online date	1 Nov 2021
DOIs	https://doi.org/10.1016/j.scienta.2021.110684 https://doi.org/10.1016/j.scienta.2021.110684
Publication status	Published - 5 Feb 2022

Access to Document

2104.05647
© 2021 The Authors
Submitted manuscript, 2.22 MB

Cite this

@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",

}

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 -

Fruit quality and defect image classification with conditional GAN data augmentation

Abstract

Access to Document

Other files and links

Fingerprint

Cite this