Deep learning-based halal food recognition

Halal is the term used for permissible food according to Islam. Indicators such as Halal logo have been used to guide Muslims in identifying Halal food. Department of Islamic Development (JAKIM) in Malaysia has introduced a standard Halal logo for locally manufactured products. Problem arises when M...

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Main Author:	Mazli, Muhamad Syafiq
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93023/1/MohamadSyafiqMazliMSKE2020.pdf
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id	my-utm-ep.93023
record_format	uketd_dc
spelling	my-utm-ep.930232021-11-07T06:00:31Z Deep learning-based halal food recognition 2020 Mazli, Muhamad Syafiq TK Electrical engineering. Electronics Nuclear engineering Halal is the term used for permissible food according to Islam. Indicators such as Halal logo have been used to guide Muslims in identifying Halal food. Department of Islamic Development (JAKIM) in Malaysia has introduced a standard Halal logo for locally manufactured products. Problem arises when Muslims in Malaysia are travelling overseas, especially to non-Latin language country. It is difficult to find the ingredients used in the product, as it is written in non-Latin language, to determine whether it is a Halal or non-Halal product. Thus, this paper proposed the use of an image recognition system in overcoming the problem by classifying between Halal and non-Halal food based on deep learning algorithm, as the number of Malaysians who travel overseas is increasing every year. Convolutional Neural Networks (CNN) deep learning method is used to recognize and classify the images into Halal and non-Halal products due to its higher accuracy on image classification. The images of product packaging are downloaded from the Google Image, augmented, resized into a 100 x 100 pixels dataset, and injected into the model by using python with package of TensorFlow. The images are taken from various products, which are available in Malaysia, to train the CNN model as a prototype. The images of overseas product packaging are expected to be added into the model for further development. A testing set, independent from training set, which are taken by camera phone, is used to test the accuracy of the trained CNN model. Multiple CNN models have been trained by tuning the number of layers and other related parameters to reach the optimum architecture for this project. CNN architecture used in this project is compromises of three convolution layers and three max pooling layers, then followed by a fully connected layer and a softmax layer. The prototype has achieved more than 90% accuracy on classifying the images of the food packaging. 2020 Thesis http://eprints.utm.my/id/eprint/93023/ http://eprints.utm.my/id/eprint/93023/1/MohamadSyafiqMazliMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135940 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mazli, Muhamad Syafiq Deep learning-based halal food recognition
description	Halal is the term used for permissible food according to Islam. Indicators such as Halal logo have been used to guide Muslims in identifying Halal food. Department of Islamic Development (JAKIM) in Malaysia has introduced a standard Halal logo for locally manufactured products. Problem arises when Muslims in Malaysia are travelling overseas, especially to non-Latin language country. It is difficult to find the ingredients used in the product, as it is written in non-Latin language, to determine whether it is a Halal or non-Halal product. Thus, this paper proposed the use of an image recognition system in overcoming the problem by classifying between Halal and non-Halal food based on deep learning algorithm, as the number of Malaysians who travel overseas is increasing every year. Convolutional Neural Networks (CNN) deep learning method is used to recognize and classify the images into Halal and non-Halal products due to its higher accuracy on image classification. The images of product packaging are downloaded from the Google Image, augmented, resized into a 100 x 100 pixels dataset, and injected into the model by using python with package of TensorFlow. The images are taken from various products, which are available in Malaysia, to train the CNN model as a prototype. The images of overseas product packaging are expected to be added into the model for further development. A testing set, independent from training set, which are taken by camera phone, is used to test the accuracy of the trained CNN model. Multiple CNN models have been trained by tuning the number of layers and other related parameters to reach the optimum architecture for this project. CNN architecture used in this project is compromises of three convolution layers and three max pooling layers, then followed by a fully connected layer and a softmax layer. The prototype has achieved more than 90% accuracy on classifying the images of the food packaging.
format	Thesis
qualification_level	Master's degree
author	Mazli, Muhamad Syafiq
author_facet	Mazli, Muhamad Syafiq
author_sort	Mazli, Muhamad Syafiq
title	Deep learning-based halal food recognition
title_short	Deep learning-based halal food recognition
title_full	Deep learning-based halal food recognition
title_fullStr	Deep learning-based halal food recognition
title_full_unstemmed	Deep learning-based halal food recognition
title_sort	deep learning-based halal food recognition
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93023/1/MohamadSyafiqMazliMSKE2020.pdf
_version_	1747818629222629376

Deep learning-based halal food recognition

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