<?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>0123-4226</journal-id>
<journal-title><![CDATA[Revista U.D.C.A Actualidad & Divulgación Científica]]></journal-title>
<abbrev-journal-title><![CDATA[rev.udcaactual.divulg.cient.]]></abbrev-journal-title>
<issn>0123-4226</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Aplicadas y Ambientales]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-42262021000200005</article-id>
<article-id pub-id-type="doi">10.31910/rudca.v24.n2.2021.1917</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicación de redes neuronales convolucionales para la detección del tizón tardío Phytophthora infestans en papa Solanum tuberosum]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lozada-Portilla]]></surname>
<given-names><![CDATA[William Alexander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Suarez-Barón]]></surname>
<given-names><![CDATA[Marco Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Avendaño-Fernández]]></surname>
<given-names><![CDATA[Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Pedagógica y Tecnológica de Colombia  ]]></institution>
<addr-line><![CDATA[Sogamoso Boyacá]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Pedagógica y Tecnológica de Colombia  ]]></institution>
<addr-line><![CDATA[Sogamoso Boyacá]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Pedagógica y Tecnológica de Colombia  ]]></institution>
<addr-line><![CDATA[Sogamoso Boyacá]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-42262021000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-42262021000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-42262021000200005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La presencia del tizón tardío o gota en el cultivo de papa afecta directamente el crecimiento de la planta y el desarrollo del tubérculo, por ello, es importante la detección temprana de la enfermedad. Actualmente, la aplicación de redes neuronales convolucionales es una oportunidad orientada a la identificación de patrones en la agricultura de precisión, incluyendo el estudio del tizón tardío, en el cultivo de papa. Este estudio describe un modelo de aprendizaje profundo capaz de reconocer el tizón tardío en el cultivo de papa, por medio de la clasificación de imágenes de las hojas. Se utilizó, en la aplicación de este modelo, el conjunto de datos aumentado de PlantVillage, para entrenamiento. El modelo propuesto ha sido evaluado a partir de métricas de rendimiento, como precisión, sensibilidad, puntaje F1 y exactitud. Para verificar la efectividad del modelo en la identificación y la clasificación del tizón tardío y comparado en rendimiento con arquitecturas. como AlexNet, ZFNet, VGG16 y VGG19. Los resultados experimentales obtenidos con el conjunto de datos seleccionado mostraron que el modelo propuesto alcanza una exactitud del 90 % y un puntaje F1, del 91 %. Por lo anterior, se concluye que el modelo propuesto es una herramienta útil para los agricultores en la identificación del tizón tardío y escalable a plataformas móviles, por la cantidad de parámetros que lo comprenden.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The presence of late blight in potato crops directly affects plant growth and tuber development; therefore, early detection of the disease is important. Currently, the application of convolutional neural networks is an opportunity oriented to the identification of patterns in precision agriculture, including the study of late blight in potato crops. This study describes a deep learning model capable of recognizing late blight in potato crops by means of leaf image classification. The PlantVillage augmented dataset was used in the application of this model for training. The proposed model has been evaluated from performance metrics such as precision, sensitivity, F1 score, and accuracy; to verify the effectiveness of the model in the identification and classification of late blight and compared in performance with architectures such as AlexNet, ZFNet, VGG16, and VGG19. The experimental results obtained with the selected data set showed that the proposed model achieves an accuracy of 90 % and an F1 score of 91 %. Therefore, it is concluded that the proposed model is a useful tool for farmers in the identification of late blight and scalable to mobile platforms due to the number of parameters that comprise it.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Agricultura de precisión]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[Redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[Tizón tardío]]></kwd>
<kwd lng="en"><![CDATA[Convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[Late blight]]></kwd>
<kwd lng="en"><![CDATA[Precision agriculture]]></kwd>
</kwd-group>
</article-meta>
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