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dc.contributor.authorFu, Yen_NZ
dc.contributor.authorNguyen, Men_NZ
dc.contributor.authorYan, WQen_NZ
dc.date.accessioned2022-05-04T02:08:52Z
dc.date.available2022-05-04T02:08:52Z
dc.date.copyright2022-07en_NZ
dc.identifier.citationSN Computer Science. 3, 264 (2022). https://doi.org/10.1007/s42979-022-01152-7
dc.identifier.issn2661-8907en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/15113
dc.description.abstractFruit freshness grading is an innate ability of humans. However, there was not much work focusing on creating a fruit grading system based on digital images in deep learning. The algorithm proposed in this article has the potentiality to be employed so as to avoid wasting fruits or save fruits from throwing away. In this article, we present a comprehensive analysis of freshness grading scheme using computer vision and deep learning. Our scheme for grading is based on visual analysis of digital images. Numerous deep learning methods are exploited in this project, including ResNet, VGG, and GoogLeNet. AlexNet is selected as the base network, and YOLO is employed for extracting the region of interest (ROI) from digital images. Therefore, we construct a novel neural network model for fruit detection and freshness grading regarding multiclass fruit classification. The fruit images are fed into our model for training, AlexNet took the leading position; meanwhile, VGG scheme performed the best in the validation.en_NZ
dc.languageenen_NZ
dc.publisherSpringer Science and Business Media LLCen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s42979-022-01152-7
dc.rightsOpen Access This article is licensed under a Creative Commons Attri bution 4.0 International License, which permits use, sharing, adapta tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.subjectCNN; Deep learning; Fruit freshness grading; YOLO; AlexNet; VGG
dc.titleGrading Methods for Fruit Freshness Based on Deep Learningen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1007/s42979-022-01152-7en_NZ
aut.relation.articlenumber264en_NZ
aut.relation.issue4en_NZ
aut.relation.volume3en_NZ
pubs.elements-id454005
aut.relation.journalSN Computer Scienceen_NZ


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