Articles producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

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

  • Datos identificativos

    Identificador: imarina:5873665
    Handle: http://hdl.handle.net/20.500.11797/imarina5873665
  • Autores:

    Lazaro A, Boada M, Villarino R, Girbau D
  • Otros:

    Autor según el artículo: Lazaro A, Boada M, Villarino R, Girbau D
    Departamento: Enginyeria Electrònica, Elèctrica i Automàtica
    Autor/es de la URV: Boada Navarro, Martí / Lázaro Guillén, Antonio Ramon / Villarino Villarino, Ramón Maria
    Palabras clave: Support vector machine (svm) Radio frequency identification (rfid) Near field communication Machine learning Internet of things (iot) Foods Food quality Energy harvesting Color sensor Classification Battery-less
    Resumen: 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.
    Áreas temáticas: Zootecnia / recursos pesqueiros Química Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Letras / linguística Interdisciplinar Instruments & instrumentation Instrumentation Information systems Geografía Geociências Farmacia Engineering, electrical & electronic Engenharias iv Engenharias iii Engenharias ii Engenharias i Electrochemistry Electrical and electronic engineering Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Biochemistry Atomic and molecular physics, and optics Astronomia / física Arquitetura, urbanismo e design Analytical chemistry
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 14248220
    Direcció de correo del autor: ramon.villarino@urv.cat antonioramon.lazaro@urv.cat
    Identificador del autor: 0000-0001-9692-8943 0000-0003-3160-5777
    Fecha de alta del registro: 2023-02-22
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/1424-8220/19/7/1741
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Sensors. 19 (7):
    Referencia de l'ítem segons les normes APA: Lazaro A, Boada M, Villarino R, Girbau D (2019). Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor. Sensors, 19(7), -. DOI: 10.3390/s19071741
    DOI del artículo: 10.3390/s19071741
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2019
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Analytical Chemistry,Atomic and Molecular Physics, and Optics,Biochemistry,Chemistry, Analytical,Electrical and Electronic Engineering,Electrochemistry,Engineering, Electrical & Electronic,Information Systems,Instrumentation,Instruments & Instrumentation,Medicine (Miscellaneous)
    Support vector machine (svm)
    Radio frequency identification (rfid)
    Near field communication
    Machine learning
    Internet of things (iot)
    Foods
    Food quality
    Energy harvesting
    Color sensor
    Classification
    Battery-less
    Zootecnia / recursos pesqueiros
    Química
    Medicine (miscellaneous)
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Linguística e literatura
    Letras / linguística
    Interdisciplinar
    Instruments & instrumentation
    Instrumentation
    Information systems
    Geografía
    Geociências
    Farmacia
    Engineering, electrical & electronic
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Electrochemistry
    Electrical and electronic engineering
    Educação física
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Chemistry, analytical
    Biotecnología
    Biodiversidade
    Biochemistry
    Atomic and molecular physics, and optics
    Astronomia / física
    Arquitetura, urbanismo e design
    Analytical chemistry
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