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Fresh fruits selection recommender

Fresh produces (fruits) quality prediction was the main idea of this project and it was hoped to serve as a fresh fruit selection recommender for common or industrial usage. With the difficulties of predicting the actual condition of the fruits through observing its external appearance as well as th...

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Main Author: Loh, Zhan Herng
Format: Final Year Project / Dissertation / Thesis
Published: 2020
Subjects:
Q Science (General)
Online Access:http://eprints.utar.edu.my/3903/1/16ACB02230_FYP.pdf
http://eprints.utar.edu.my/3903/
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spelling my-utar-eprints.39032021-01-07T06:46:27Z Fresh fruits selection recommender Loh, Zhan Herng Q Science (General) Fresh produces (fruits) quality prediction was the main idea of this project and it was hoped to serve as a fresh fruit selection recommender for common or industrial usage. With the difficulties of predicting the actual condition of the fruits through observing its external appearance as well as the internal quality factors, believing that Computer Vision could help us to solve the problem. In this project, the fruit domains involved were apple, banana and orange. Generally, the development mainly split into two different tasks or phases, fruit classification and fruit detection or localization. To identify and localize the fruit presented in one image or frame, another set of data image was collected to make annotations manually and these data were prepared to “feed” into the object detection API, which Faster R-CNN object detection model was configured as the training pipeline. A frozen inference graph was trained for fruit detection usage. Besides, to make predictions on the freshness states of these fruits, data images of two distinguishable freshness states of ‘Fresh’ and ‘Rotten’ was collected. Convolutional Neural Network (CNN) architecture was used to construct and train for classification model. In order to achieve desirable performance from the model, evaluations and analysis were made so as to make incremental improvements through using regularization method as well as transfer learning approach. To conclude the achievements of both model, the Faster R-CNN detection inference graph was welltrained as it identifies classes at most of the situations, as for the CNN classification model, it achieved the overall accuracy of 99.81% because of leveraging the pre-trained weights from VGG-16 model. Till current stage, the prototype was usable for limited fruit domains quality prediction. It was believed that it could be deployed for real-world usage if improvements and extended development were made on this system prototype. 2020-05-14 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3903/1/16ACB02230_FYP.pdf Loh, Zhan Herng (2020) Fresh fruits selection recommender. Final Year Project, UTAR. http://eprints.utar.edu.my/3903/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
spellingShingle Q Science (General)
Loh, Zhan Herng
Fresh fruits selection recommender
description Fresh produces (fruits) quality prediction was the main idea of this project and it was hoped to serve as a fresh fruit selection recommender for common or industrial usage. With the difficulties of predicting the actual condition of the fruits through observing its external appearance as well as the internal quality factors, believing that Computer Vision could help us to solve the problem. In this project, the fruit domains involved were apple, banana and orange. Generally, the development mainly split into two different tasks or phases, fruit classification and fruit detection or localization. To identify and localize the fruit presented in one image or frame, another set of data image was collected to make annotations manually and these data were prepared to “feed” into the object detection API, which Faster R-CNN object detection model was configured as the training pipeline. A frozen inference graph was trained for fruit detection usage. Besides, to make predictions on the freshness states of these fruits, data images of two distinguishable freshness states of ‘Fresh’ and ‘Rotten’ was collected. Convolutional Neural Network (CNN) architecture was used to construct and train for classification model. In order to achieve desirable performance from the model, evaluations and analysis were made so as to make incremental improvements through using regularization method as well as transfer learning approach. To conclude the achievements of both model, the Faster R-CNN detection inference graph was welltrained as it identifies classes at most of the situations, as for the CNN classification model, it achieved the overall accuracy of 99.81% because of leveraging the pre-trained weights from VGG-16 model. Till current stage, the prototype was usable for limited fruit domains quality prediction. It was believed that it could be deployed for real-world usage if improvements and extended development were made on this system prototype.
format Final Year Project / Dissertation / Thesis
author Loh, Zhan Herng
author_facet Loh, Zhan Herng
author_sort Loh, Zhan Herng
title Fresh fruits selection recommender
title_short Fresh fruits selection recommender
title_full Fresh fruits selection recommender
title_fullStr Fresh fruits selection recommender
title_full_unstemmed Fresh fruits selection recommender
title_sort fresh fruits selection recommender
publishDate 2020
url http://eprints.utar.edu.my/3903/1/16ACB02230_FYP.pdf
http://eprints.utar.edu.my/3903/
_version_ 1688551790359871488
score 13.211869

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