Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli

Image recognition is one of the most crucial fields of computer vision and processing of images. Classification of the food image is a special feature of an image recognition problem. Individuals are more aware of their health in modern times. Moreover, food is also an important part of community an...

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Main Author: Rusli, Siti Nur Hidayah
Format: Student Project
Language:English
Published: 2021
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Online Access:http://ir.uitm.edu.my/id/eprint/46068/1/46068.pdf
http://ir.uitm.edu.my/id/eprint/46068/
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spelling my.uitm.ir.460682021-06-22T04:09:07Z http://ir.uitm.edu.my/id/eprint/46068/ Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli Rusli, Siti Nur Hidayah Information technology. Information systems Pattern recognition systems Image recognition is one of the most crucial fields of computer vision and processing of images. Classification of the food image is a special feature of an image recognition problem. Individuals are more aware of their health in modern times. Moreover, food is also an important part of community and identity. Through food, we can get to know a country and its people. In recent years it has become quite clear that many Malaysians do not familiar with and recognize traditional food especially green leafy vegetables. Nevertheless, it is also very regrettable that the younger generation will never really grasp the types of green leafy vegetables. One of the promising approaches to acknowledge the problems is using technology which is Tensor flow to assist individuals in recognizing the heritage in Malaysia especially on green leafy vegetables such as mustard green, cilantro, chives, green onion and soup leaves. To classify image recognition, the researcher used the convolutional neural network. CNN`s are a very effective class of neural networks that are highly effective in classifying images, detecting objects and other computer vision problems. The researcher categorizes a vegetables and non-vegetables dataset with 1125 photos, consisting of different types of vegetables and non-vegetables. Moreover, the recognition system will recognize the images using GUI and python command prompt. The result of the recognition of vegetables will be stored in the database and details of the recognition of vegetables will be shown on the website. Accuracy, precision and recall tests used to determine the accuracy of the system and will improve the user satisfaction when user used the green leafy vegetables image recognition prototype. Lastly, the accuracy obtained in this green leafy image recognition system is 98%. 2021-05-05 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/46068/1/46068.pdf ID46068 Rusli, Siti Nur Hidayah (2021) Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli. [Student Project] (Unpublished)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Information technology. Information systems
Pattern recognition systems
spellingShingle Information technology. Information systems
Pattern recognition systems
Rusli, Siti Nur Hidayah
Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli
description Image recognition is one of the most crucial fields of computer vision and processing of images. Classification of the food image is a special feature of an image recognition problem. Individuals are more aware of their health in modern times. Moreover, food is also an important part of community and identity. Through food, we can get to know a country and its people. In recent years it has become quite clear that many Malaysians do not familiar with and recognize traditional food especially green leafy vegetables. Nevertheless, it is also very regrettable that the younger generation will never really grasp the types of green leafy vegetables. One of the promising approaches to acknowledge the problems is using technology which is Tensor flow to assist individuals in recognizing the heritage in Malaysia especially on green leafy vegetables such as mustard green, cilantro, chives, green onion and soup leaves. To classify image recognition, the researcher used the convolutional neural network. CNN`s are a very effective class of neural networks that are highly effective in classifying images, detecting objects and other computer vision problems. The researcher categorizes a vegetables and non-vegetables dataset with 1125 photos, consisting of different types of vegetables and non-vegetables. Moreover, the recognition system will recognize the images using GUI and python command prompt. The result of the recognition of vegetables will be stored in the database and details of the recognition of vegetables will be shown on the website. Accuracy, precision and recall tests used to determine the accuracy of the system and will improve the user satisfaction when user used the green leafy vegetables image recognition prototype. Lastly, the accuracy obtained in this green leafy image recognition system is 98%.
format Student Project
author Rusli, Siti Nur Hidayah
author_facet Rusli, Siti Nur Hidayah
author_sort Rusli, Siti Nur Hidayah
title Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli
title_short Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli
title_full Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli
title_fullStr Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli
title_full_unstemmed Image recognition of green leafy vegetables using tensor flow / Siti Nur Hidayah Rusli
title_sort image recognition of green leafy vegetables using tensor flow / siti nur hidayah rusli
publishDate 2021
url http://ir.uitm.edu.my/id/eprint/46068/1/46068.pdf
http://ir.uitm.edu.my/id/eprint/46068/
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score 13.160551