<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432022000400107</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2022.23.4.031</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Integración de una cámara multiespectral y aprendizaje automático para clasificación de manzanas]]></article-title>
<article-title xml:lang="en"><![CDATA[Integration of a multispectral camera and machine learning for apple sorting]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gante-Díaz]]></surname>
<given-names><![CDATA[Saulo Abraham]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lozano-Hernández]]></surname>
<given-names><![CDATA[Yair]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Maldonado-Trinidad]]></surname>
<given-names><![CDATA[Marco Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Flores-Colunga]]></surname>
<given-names><![CDATA[Gerardo Ramón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villegas-Piña]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Ingeniería Mecánica y Eléctrica ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Ingeniería Mecánica y Eléctrica ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Ingeniería Mecánica y Eléctrica ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Centro de Investigaciones en Óptica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="Af5">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Ingeniería Mecánica y Eléctrica ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>23</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432022000400107&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432022000400107&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432022000400107&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este trabajo presenta un sistema capaz de realizar una clasificación binaria (buena y podrida) de manzanas red delicious, lo cual se logra mediante el uso del Índice de Vegetación de Diferencia Normalizada (NDVI) y una Red Neural Xception. Para ello, se utiliza una cámara multiespectral para observar detalles fuera del alcance del ojo humano, reduciendo la presencia de errores durante su clasificación. La selección del NDVI es resultado de su comparación con los índices de vegetación GNDVI, GVI, NDRE, NDVIR, NG, NGRDI y RVI, aplicados a un banco de imágenes obtenido mediante la cámara multiespectral. Además, se muestran los resultados de la clasificación al utilizar redes neuronales Xception, ResNet y MobileNet, lo que justifica el uso de la red Xception. Finalmente se describe la instrumentación e iluminación empleada en un prototipo que emula el proceso de clasificación real utilizando una banda transportadora, lo que permite validar de forma experimental el sistema de clasificación propuesto, obteniéndose un 73 % de éxito en la clasificación en línea. El desarrollo de este trabajo se basa en la siguiente metodología: Se utiliza una cámara multiespectral para la creación de una base de datos, las imágenes obtenidas pasan a una etapa de procesamiento conformada por la alineación y reconstrucción RGB. Posteriormente, se utilizan dos o más bandas para el cálculo, comparación y análisis de diferentes índices de vegetación. Una vez determinado el índice de vegetación a utilizar, se procede al entrenamiento y comparativa entre distintas arquitecturas de redes neuronales. Respecto a la etapa de entrenamiento, se emplea transferencia de aprendizaje para reducir la necesidad de una gran base de datos. Por último, se realizan pruebas experimentales para validar el comportamiento.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This paper presents a system capable of performing a binary classification (ripe and rotten) of red apples, which is achieved through the use of the Normalized Difference Vegetation Index (NDVI) and an Xception Neural Network. In order to carry out this process a multispectral camera is used to observe details outside the scope of the human eye, reducing the presence of errors during their classification. The selection of NDVI is the result of its comparison with the GNDVI, GVI, NDRE, NDVIR, NG, NGRDI, and RVI vegetation indices applied to an image bank obtained by means of the multispectral camera. Furthermore, classification results are displayed when using Xception, ResNet, and MobileNet neural networks, thus justifying the use of the Xception network. Finally, the instrumentation and lighting used in a prototype, which emulates the actual classification process using a conveyor belt, are described. This method allows to experimentally validate the proposed classification system, in this case, 73 % success in the online classification was achieved. The development of this work is based on the following methodology: a multispectral camera is used to create a database, the obtained images are sent to a processing stage, which involves alignment and RGB reconstruction. Subsequently, two or more bands are used for the calculation, comparison and analysis of different vegetation indices. Once the vegetation index is established, the training and comparison among different neural network architectures is carried out. Regarding the training stage, transfer learning is used to reduce the need for a large database. Finally, experimental tests are carried out to validate the behaviour.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Clasificación de manzanas]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[cámara multiespectral]]></kwd>
<kwd lng="es"><![CDATA[índice de vegetación]]></kwd>
<kwd lng="en"><![CDATA[Apple sorting]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[multispectral camera]]></kwd>
<kwd lng="en"><![CDATA[vegetation index]]></kwd>
</kwd-group>
</article-meta>
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