AUT LibraryAUT
View Item 
  •   Open Theses & Dissertations
  • Masters Theses
  • View Item
  •   Open Theses & Dissertations
  • Masters Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Fruit Freshness Grading Using Deep Learning

Fu, Yuhang
Thumbnail
View/Open
Thesis (4.356Mb)
Permanent link
http://hdl.handle.net/10292/13353
Metadata
Show full metadata
Abstract
This thesis presents a comprehensive analysis of a variety of fruit images for freshness grading using deep learning. A number of algorithms have been reviewed in this project, including YOLO for detecting region of interest with considerations of digital images, ResNet, VGG, Google Net, and AlexNet as the base networks for freshness grading feature extraction. Fruit decaying occurs in a gradual manner, this characteristic is included for freshness grading by interpreting chronologically-related fruit decaying information.

The contribution of this thesis is to propose a novel neural network structure, i.e., YOLO + Regression CNNs for fruit object locating, classification, and freshness grading. Fruits as an object, its images are fed into YOLO for segmentation and regression, then for freshness grading. The results reveal that our approach outperforms linear predictive model and demonstrate its special merit.
Keywords
CNN; YOLO; Deep Learning; Fruit Freshness; Regression; Image Recognition
Date
2020
Item Type
Thesis
Supervisor(s)
Yan, Wei Qi
Degree Name
Master of Computer and Information Sciences
Publisher
Auckland University of Technology

Contact Us
  • Admin

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

 

 

Browse

Open Theses & DissertationsTitlesAuthorsDateThesis SupervisorMasters ThesesTitlesAuthorsDateThesis Supervisor

Alternative metrics

 

Statistics

For this itemFor all Open Theses & Dissertations

Share

 
Follow @AUT_SC

Contact Us
  • Admin

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