Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor.

Internet of Things (IoT) Near Field Communication Radio Frequency Identification (RFID) Support Vector Machine (SVM) battery-less classification color sensor energy harvesting food quality machine learning

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
11 Apr 2019
Historique:
received: 08 03 2019
revised: 03 04 2019
accepted: 07 04 2019
entrez: 14 4 2019
pubmed: 14 4 2019
medline: 10 8 2019
Statut: epublish

Résumé

This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used.

Identifiants

pubmed: 30979009
pii: s19071741
doi: 10.3390/s19071741
pmc: PMC6479777
pii:
doi:

Types de publication

Journal Article

Langues

eng

Références

J Agric Food Chem. 2008 Jul 23;56(14):5723-31
pubmed: 18582083
Sensors (Basel). 2017 Nov 01;17(11):
pubmed: 29104212
Sensors (Basel). 2018 Nov 02;18(11):null
pubmed: 30400233

Auteurs

Antonio Lazaro (A)

Department of Electronic, Electric and Automatic Control Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain. antonioramon.lazaro@urv.cat.

Marti Boada (M)

Department of Electronic, Electric and Automatic Control Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain. marti.boada@urv.cat.

Ramon Villarino (R)

Department of Electronic, Electric and Automatic Control Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain. ramon.villarino@urv.cat.

David Girbau (D)

Department of Electronic, Electric and Automatic Control Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain. david.girbau@urv.cat.

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