Ambient-Aware Food Recognition and Management in Context of Smart Fridge
Object detection and image classification is one of the modern computer vision technology. Image classification can be defined as the process of labelling images into one of a number of predefined classes or categories. Image classification can be done using several classification techniques such as...
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my-utar-eprints.37622020-12-30T11:06:51Z Ambient-Aware Food Recognition and Management in Context of Smart Fridge Lau, Christine Yuet Ning T Technology (General) Object detection and image classification is one of the modern computer vision technology. Image classification can be defined as the process of labelling images into one of a number of predefined classes or categories. Image classification can be done using several classification techniques such as Faster-RCNN, YOLO and SSD. This project is an image classification project for detecting the life time of a fruit. The project is able to detect the remaining life time of the fruit before it is not edible based on the fruit’s current state. In this project, Faster-RCNN was used to carry out image classification. Food wastage is now causing a big problem to the world. Land has been deforested,animal species has been driven to extinction and soil has been degraded just for producing food that are never to be eaten. If not taken care of, it will cause bigger problems to the world and society. With technology, this project was carried out to reduce food wastage by using image classification with Faster-RCNN. The final product is able to tell the users the remaining days of the fruit to be consumed before the fruit is going bad. The result of the project is expressed in the report. 2020-05-14 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3762/1/16ACB03254_FYP2.pdf Lau, Christine Yuet Ning (2020) Ambient-Aware Food Recognition and Management in Context of Smart Fridge. Final Year Project, UTAR. http://eprints.utar.edu.my/3762/ |
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T Technology (General) Lau, Christine Yuet Ning Ambient-Aware Food Recognition and Management in Context of Smart Fridge |
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Object detection and image classification is one of the modern computer vision technology. Image classification can be defined as the process of labelling images into one of a number of predefined classes or categories. Image classification can be done using several classification techniques such as Faster-RCNN, YOLO and SSD. This project is an image classification project for detecting the life time of a fruit. The project is able to detect the remaining life time of the fruit before it is not edible based on the fruit’s current state. In this
project, Faster-RCNN was used to carry out image classification.
Food wastage is now causing a big problem to the world. Land has been deforested,animal species has been driven to extinction and soil has been degraded just for producing food that are never to be eaten. If not taken care of, it will cause bigger problems to the world and
society. With technology, this project was carried out to reduce food wastage by using image classification with Faster-RCNN. The final product is able to tell the users the remaining days of the fruit to be consumed before the fruit is going bad. The result of the project is expressed in the report. |
format |
Final Year Project / Dissertation / Thesis |
author |
Lau, Christine Yuet Ning |
author_facet |
Lau, Christine Yuet Ning |
author_sort |
Lau, Christine Yuet Ning |
title |
Ambient-Aware Food Recognition and Management in Context of Smart Fridge |
title_short |
Ambient-Aware Food Recognition and Management in Context of Smart Fridge |
title_full |
Ambient-Aware Food Recognition and Management in Context of Smart Fridge |
title_fullStr |
Ambient-Aware Food Recognition and Management in Context of Smart Fridge |
title_full_unstemmed |
Ambient-Aware Food Recognition and Management in Context of Smart Fridge |
title_sort |
ambient-aware food recognition and management in context of smart fridge |
publishDate |
2020 |
url |
http://eprints.utar.edu.my/3762/1/16ACB03254_FYP2.pdf http://eprints.utar.edu.my/3762/ |
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1688551769290833920 |
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13.214268 |