<?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>0187-7380</journal-id>
<journal-title><![CDATA[Revista fitotecnia mexicana]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. fitotec. mex]]></abbrev-journal-title>
<issn>0187-7380</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Fitogenética A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-73802022000300369</article-id>
<article-id pub-id-type="doi">10.35196/rfm.2022.3.369</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificación de manzanas con redes neuronales convolucionales]]></article-title>
<article-title xml:lang="en"><![CDATA[Classification of apples with convolutional neuronal networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Olguín-Rojas]]></surname>
<given-names><![CDATA[Juan C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vasquez-Gomez]]></surname>
<given-names><![CDATA[Juan I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Canteñs]]></surname>
<given-names><![CDATA[Gilberto de J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera-Lozada]]></surname>
<given-names><![CDATA[Juan C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Centro de Innovación y Desarrollo Tecnológico en Cómputo ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma Chapingo Departamento de Ingeniería Mecánica Agrícola ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<volume>45</volume>
<numero>3</numero>
<fpage>369</fpage>
<lpage>378</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-73802022000300369&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-73802022000300369&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-73802022000300369&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Actualmente, en puntos de venta y en empresas agroindustriales de México, la clasificación de manzanas (Malus domestica) la realizan personas de forma manual, lo que genera deficiencias en la calidad del producto. Estos problemas se pueden reducir con la implementación de equipos de visión en sitio equipados con algoritmos de aprendizaje automático. En este estudio se analizaron varias arquitecturas de red neuronal convolucional (CNN) y se seleccionó una que permite clasificar manzanas en sanas y dañadas en el proceso en postcosecha. Las variedades utilizadas fueron Red Delicious, Granny Smith, Golden Delicious y Gala. Se comparó la exactitud de las CNN LeNet5 y VGG16. Se realizó una serie de tratamientos (combinación de red con hiperparámetros) que se utilizaron para la clasificación del objeto de estudio. Al probarse cada tratamiento se midió su rendimiento. Al finalizar, el tratamiento con mejor rendimiento fue LeNet5 entrenada desde cero con el optimizador RMSProp, que obtuvo una exactitud del 97 %.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary Nowadays, in points of sale and in agro-industrial companies in Mexico, the classification of apples (Malus domestica) is carried out manually by people, which generates deficiencies in the quality of the product. These problems can be reduced with the implementation of in site vision equipment with machine learning algorithms. In this study, several convolutional neuronal network (CNN) architectures were analyzed and one of those was selected that allows apples to be classified into healthy and damaged in the postharvest process. The varieties used were Red Delicious, Granny Smith, Golden Delicious and Gala. The accuracy of the LeNet5 and VGG16 CNNs was compared. A series of treatments (combination of network with hyperparameters) was performed that were used for the classification of the object of study. As each treatment was tested, its performance was measured. At the end, the treatment with the best performance was LeNet5 trained from scratch with the RMSProp optimizer, which obtained an accuracy of 97 %.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Malus domestica]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[LeNet5]]></kwd>
<kwd lng="es"><![CDATA[VGG16]]></kwd>
<kwd lng="en"><![CDATA[Malus domestica]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[LeNet5]]></kwd>
<kwd lng="en"><![CDATA[VGG16]]></kwd>
</kwd-group>
</article-meta>
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