Autonomous Fruit Recognition System based on Deep Convolutional Neural Network

Authors

  • Sahana P. Savant  PG Scholar, Computer science department, KLS Gogte Institute of Technology, Belgaum, Karnataka, India
  • P. S. Khanagoudar  Assistant Professor Computer Science department, KLS Gogte Institute of Technology, Belgaum, Karnataka, India

DOI:

https://doi.org//10.32628/IJSRSET2072104

Keywords:

Fruit Recognition/ DCNN/ Machine vision/Calorie measurement/MobileNet.

Abstract

Recently it is found that people are becoming more cautious to their diet throughout the universe. Unhealthy diet can cause many problems like sugar, obesity, gain in weight and many other chronic health related issues. Essential part of our diet is contributed by fruits as they are rich source of vitamins,fiber,energy and nutrients. Today's era has been adapted to a system of intake of foods which has several adverse effects on human health. The proposed system is Autonomous Fruit Recognition system based on Deep Convolutional Neural Network (DCNN) method. Using this technology recognition and estimation of fruit calories is necessary to spread awareness about food habits among people suffering from obesity due to bad food culture and consumption of food .This proposed web/app based system simplifies the calorie measuring process of fruit. The machine learning based API used in our system recognize the fruit and provide calorie content of that fruit. System uses convolutional Neural Network called MobileNet. This web/app based application is user friendly.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-04-30

Issue

Volume 7, Issue 2, March-April-2020

Section

Research Articles

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How to Cite

[1]
Sahana P. Savant, P. S. Khanagoudar, " Autonomous Fruit Recognition System based on Deep Convolutional Neural Network, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 2, pp.666-669, March-April-2020. Available at doi : https://doi.org/10.32628/IJSRSET2072104