Fruits and vegetables calorie counter using Convolutional neural networks

Morteza Akbari Fard, Hamed Haddadi, Alireza Tavakoli Targhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Individuals care about the types of fruit they are eating and the nutrients it contains because eating fruits and vegetables is an essential part of leading a healthy diet. In this paper we introduce an automatic way for detecting and recognizing the fruits in an image in order to enable keeping track of daily intake automatically using images taken by the user. The proposed method uses state of the art deep-learning techniques for feature extraction and classification. Deep learning methods, especially convolutional neural networks, have been widely used for a variety of classification problems and have achieved promising results. Our trained model has achieved an accuracy of 75% in the task of classification of 43 different types of fruit. The similar methods have achieved up to 70% with fewer classes.

Original languageEnglish
Title of host publicationDH 2016 - Proceedings of the 2016 Digital Health Conference
PublisherAssociation for Computing Machinery, Inc
Pages121-122
Number of pages2
ISBN (Electronic)9781450342247
DOIs
Publication statusPublished - 11 Apr 2016
Event6th International Conference on Digital Health, DH 2016 - Montreal, Canada
Duration: 11 Apr 201613 Apr 2016

Publication series

NameDH 2016 - Proceedings of the 2016 Digital Health Conference

Other

Other6th International Conference on Digital Health, DH 2016
CountryCanada
CityMontreal
Period11/4/1613/4/16

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Keywords

  • Calorie counter
  • Convolutional neural network
  • Deep learning
  • Fruit classification
  • Fruit recognition

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications
  • Health Informatics

Cite this

Fard, M. A., Haddadi, H., & Targhi, A. T. (2016). Fruits and vegetables calorie counter using Convolutional neural networks. In DH 2016 - Proceedings of the 2016 Digital Health Conference (pp. 121-122). (DH 2016 - Proceedings of the 2016 Digital Health Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2896338.2896355