FridgeSnap: A software for recipe suggestion based on food image classification

Liam Boyd; Nonso Nnamoko

doi:10.1016/j.simpa.2023.100585

FridgeSnap: A software for recipe suggestion based on food image classification

Liam Boyd
, Nonso Nnamoko^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (journal) › peer-review

2 Citations (Scopus)

90 Downloads (Pure)

Abstract

Food waste is a global issue, affecting both consumers and producers, with heavy economical and environmental impact. This paper presents FridgeSnap, a tool based on image classification that has the potential to help reduce the global food waste. The tool receives as input, images of singular food items taken from users’ electronic device such as mobile phone or tablet, then identifies the constituent food item through an underlying deep learning model before suggesting possible recipes that can be made with the food item. The application was built within the Android Studio IDE using Java programming languages and XML, thus works on Android devices. A potentially shippable version of the FridgeSnap including source code and the android application package (APK) file, is freely available on Github

Original language	English
Article number	100585
Pages (from-to)	1-5
Journal	Software Impacts
Volume	18
Early online date	26 Sept 2023
DOIs	https://doi.org/10.1016/j.simpa.2023.100585 https://doi.org/10.1016/j.simpa.2023.100585
Publication status	Published - 30 Nov 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action

Keywords

Android application
Deep learning
Food waste
Image classification
Recipe recommender

Access to Document

1-s2.0-S2665963823001227-mainFinal published version, 735 KBLicence: CC BY

Cite this

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abstract = "Food waste is a global issue, affecting both consumers and producers, with heavy economical and environmental impact. This paper presents FridgeSnap, a tool based on image classification that has the potential to help reduce the global food waste. The tool receives as input, images of singular food items taken from users{\textquoteright} electronic device such as mobile phone or tablet, then identifies the constituent food item through an underlying deep learning model before suggesting possible recipes that can be made with the food item. The application was built within the Android Studio IDE using Java programming languages and XML, thus works on Android devices. A potentially shippable version of the FridgeSnap including source code and the android application package (APK) file, is freely available on Github",

keywords = "Android application, Deep learning, Food waste, Image classification, Recipe recommender",

author = "Liam Boyd and Nonso Nnamoko",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

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N2 - Food waste is a global issue, affecting both consumers and producers, with heavy economical and environmental impact. This paper presents FridgeSnap, a tool based on image classification that has the potential to help reduce the global food waste. The tool receives as input, images of singular food items taken from users’ electronic device such as mobile phone or tablet, then identifies the constituent food item through an underlying deep learning model before suggesting possible recipes that can be made with the food item. The application was built within the Android Studio IDE using Java programming languages and XML, thus works on Android devices. A potentially shippable version of the FridgeSnap including source code and the android application package (APK) file, is freely available on Github

AB - Food waste is a global issue, affecting both consumers and producers, with heavy economical and environmental impact. This paper presents FridgeSnap, a tool based on image classification that has the potential to help reduce the global food waste. The tool receives as input, images of singular food items taken from users’ electronic device such as mobile phone or tablet, then identifies the constituent food item through an underlying deep learning model before suggesting possible recipes that can be made with the food item. The application was built within the Android Studio IDE using Java programming languages and XML, thus works on Android devices. A potentially shippable version of the FridgeSnap including source code and the android application package (APK) file, is freely available on Github

KW - Android application

KW - Deep learning

KW - Food waste

KW - Image classification

KW - Recipe recommender

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ER -

FridgeSnap: A software for recipe suggestion based on food image classification

Abstract

UN SDGs

Keywords

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