Fruit and vegetable information system using embedded convolutional neural networks

Muñoz Bocanegra, Ricardo (Universidad Autónoma de Occidente, Department of Electrical and Automation, Cali, Colombia) ; López Sotelo, Jesús Alfonso (Universidad Autónoma de Occidente, Department of Electrical and Automation, Cali, Colombia) ; Satizabal, Héctor (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Perez-Uribe, Andres (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

This article presents the development of a mobile application that exploits a Convolutional Neural Network (CNN) to recognize a set of fruits and vegetables by processing snapshots taken by the built-in camera of the device. We built an acquisition system to gather pictures of different kinds of fruits and vegetables to train a neural network model. Instead of defining a new topology and training it from scratch, we took advantage of transfer learning and fine-tuned several MobileNet models to classify our images in their corresponding classes on a smartphone. Once the fruit or vegetable is identified, our mobile application provides valuable nutritional information about it.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Guayaquil, Ecuador, 11-15 November 2019
Date:
2019-11
Guayaquil, Ecuador
11-15 November 2019
Pagination:
5 p.
Published in:
Proceedings of 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 11-15 November 2019, Guayaquil, Ecuador
DOI:
ISBN:
978-1-7281-5666-8
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-05-19, last modified 2020-05-22

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