AUT LibraryAUT
View Item 
  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
  • View Item
  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Grading Methods for Fruit Freshness Based on Deep Learning

Fu, Y; Nguyen, M; Yan, WQ
Thumbnail
View/Open
Journal article (2.052Mb)
Permanent link
http://hdl.handle.net/10292/15113
Metadata
Show full metadata
Abstract
Fruit 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.
Keywords
CNN; Deep learning; Fruit freshness grading; YOLO; AlexNet; VGG
Date
July 2022
Source
SN Computer Science. 3, 264 (2022). https://doi.org/10.1007/s42979-022-01152-7
Item Type
Journal Article
Publisher
Springer Science and Business Media LLC
DOI
10.1007/s42979-022-01152-7
Publisher's Version
https://link.springer.com/article/10.1007/s42979-022-01152-7
Rights Statement
Open 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/.

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library

 

 

Browse

Open ResearchTitlesAuthorsDateSchool of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, PāngarauTitlesAuthorsDate

Alternative metrics

 

Statistics

For this itemFor all Open Research

Share

 
Follow @AUT_SC

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library