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dc.contributor.advisorYan, Wei Qi
dc.contributor.authorFu, Yuhang
dc.date.accessioned2020-05-26T02:46:32Z
dc.date.available2020-05-26T02:46:32Z
dc.date.copyright2020
dc.identifier.urihttp://hdl.handle.net/10292/13353
dc.description.abstractThis 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.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectCNNen_NZ
dc.subjectYOLOen_NZ
dc.subjectDeep Learningen_NZ
dc.subjectFruit Freshnessen_NZ
dc.subjectRegressionen_NZ
dc.subjectImage Recognitionen_NZ
dc.titleFruit Freshness Grading Using Deep Learningen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2020-05-24T02:20:35Z


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