Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network

Malaysia is still a net importer of both fresh and refined fruits and the fresh fruit export price is around USD 174 million. Various methods are presented to improve fruit and vegetable production. Using the latest technologies and knowledge-based production systems, conventional farms will b...

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Main Author: Aziz, Amir Aizat
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20890 /
Published: IRC 2019
Subjects:
Q Science (General)
Online Access: http://utpedia.utp.edu.my/20890/1/AMIR%20AIZAT_23010.pdf
http://utpedia.utp.edu.my/20890/
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recordtype eprints
spelling utp-utpedia.208902021-09-09T19:57:59Z http://utpedia.utp.edu.my/20890/ Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network Aziz, Amir Aizat Q Science (General) Malaysia is still a net importer of both fresh and refined fruits and the fresh fruit export price is around USD 174 million. Various methods are presented to improve fruit and vegetable production. Using the latest technologies and knowledge-based production systems, conventional farms will be turned into sustainable farms. Since consumers use the appearance of fruits to first evaluate the quality of fresh food, the presence of skin defects appears to be one of the most influential factors in fresh food quality and price. For this purpose, packing houses need suitable systems capable of detecting skin deficiencies in fruits. The problem statement of this study is packing houses do not have a proper method that can identify the deficiencies in fruits using computer vision. It also involved computer vision technology approach and machine learning doing supervised learning that used Faster R-CNN model as element to achieve the objective of the project. Hence, this study been conducted to develop a method to classify the types of fruits and identify defect of the fruits based on their outer skin. IRC 2019-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20890/1/AMIR%20AIZAT_23010.pdf Aziz, Amir Aizat (2019) Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic Q Science (General)
spellingShingle Q Science (General)
Aziz, Amir Aizat
Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
description Malaysia is still a net importer of both fresh and refined fruits and the fresh fruit export price is around USD 174 million. Various methods are presented to improve fruit and vegetable production. Using the latest technologies and knowledge-based production systems, conventional farms will be turned into sustainable farms. Since consumers use the appearance of fruits to first evaluate the quality of fresh food, the presence of skin defects appears to be one of the most influential factors in fresh food quality and price. For this purpose, packing houses need suitable systems capable of detecting skin deficiencies in fruits. The problem statement of this study is packing houses do not have a proper method that can identify the deficiencies in fruits using computer vision. It also involved computer vision technology approach and machine learning doing supervised learning that used Faster R-CNN model as element to achieve the objective of the project. Hence, this study been conducted to develop a method to classify the types of fruits and identify defect of the fruits based on their outer skin.
format Final Year Project
author Aziz, Amir Aizat
author_sort Aziz, Amir Aizat
title Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
title_short Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
title_full Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
title_fullStr Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
title_full_unstemmed Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
title_sort fruit classification and defect detection system using faster region convolutional neural network
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20890/1/AMIR%20AIZAT_23010.pdf
http://utpedia.utp.edu.my/20890/
_version_ 1741195678040719360
score 11.62408

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