<?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>S0187-73802025000300303</article-id>
<article-id pub-id-type="doi">10.35196/rfm.2025.3.303</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Identificación de problemas fitosanitarios en el cultivo de fresa por medio de redes neuronales convolucionales híbridas]]></article-title>
<article-title xml:lang="en"><![CDATA[Identification of plant health problems in strawberry using hybrid convolutional neural networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montes-Rodriguez]]></surname>
<given-names><![CDATA[Giovanny]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Camacho]]></surname>
<given-names><![CDATA[Juan Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<volume>48</volume>
<numero>3</numero>
<fpage>303</fpage>
<lpage>312</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-73802025000300303&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-73802025000300303&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-73802025000300303&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La aplicación de nuevas tecnologías para diagnóstico oportuno de problemas fitosanitarios contribuye a mejorar el manejo de los cultivos agrícolas; en particular, los métodos de inteligencia artificial y visión por computadora facilitan el reconocimiento de enfermedades en cultivos de forma automática y confiable. En esta investigación se implementaron dos arquitecturas de redes neuronales convolucionales (CNN) híbridas para identificar 10 clases objetivo: tres enfermedades en frutos de fresa, antracnosis (Colletotrichum spp.), cenicilla (Sphaerotheca macularis) y moho gris (Botrytis cinerea); cuatro enfermedades en hojas de fresa, mancha angular (Xanthomonas fragariae), viruela (Ramularia tulasnei), cenicilla (Sphaerotheca macularis) y quemadura de la hoja (Diplocarpon earlianum); deficiencia nutricional de calcio en hojas de fresa, hojas sanas y frutos sanos. En la primera etapa se entrenó el modelo CNN con transferencia de aprendizaje MobileNetv2 (CNN-M) a partir de un conjunto de imágenes digitales RGB para extraer en forma automática un total de 62,720 características, a las cuales se aplicó un análisis de componentes principales. En la segunda etapa, las características transformadas en la última capa de neuronas del modelo CNN-M se utilizaron como entradas para optimizar los modelos de aprendizaje bosque aleatorio (RF) y máquina de soporte vectorial (SVM) y predecir las 10 clases objetivo. Los modelos CNN híbridos alcanzaron una precisión global de clasificación (ACC) superior a 97 %. CNN-M-SVM superó ligeramente a CNN-M-RF con un puntaje promedio F1macro de 97.6 % y ACC de 98.9 %. Los resultados muestran el alto potencial de los modelos convolucionales híbridos con transferencia de aprendizaje para desarrollar herramientas de identificación automática de enfermedades en cultivos de interés agrícola.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary The application of new technologies for early diagnosis of plant health problems contributes to improved management of agricultural crops. In particular, artificial intelligence and computer vision methods facilitate automatic and reliable recognition of crop diseases. In this research, two hybrid convolutional neural network (CNN) architectures were implemented to identify 10 target classes: three strawberry fruit diseases: anthracnose (Colletotrichum spp.), powdery mildew (Sphaerotheca macularis) and gray mold (Botrytis cinerea); four strawberry leaf diseases: angular leaf spot (Xanthomonas fragariae), white spot (Ramularia tulasnei), powdery mildew (Sphaerotheca macularis) and leaf scorch (Diplocarpon earlyum), as well as calcium nutritional deficiency in strawberry leaves, healthy leaves, and healthy fruits. In the first stage, the CNN model was trained using MobileNetv2 (CNN-M) learning transfer from a set of digital RGB images to automatically extract a total of 62,720 features, to which a principal component analysis was applied. In the second stage, the transformed features from the last neural layer of the CNN-M model were used as inputs to optimize the random forest (RF) and vector support machine (SVM) learning models and predict the 10 target classes. The hybrid CNN models achieved an overall classification accuracy (ACC) above 97 %. CNN-M-SVM slightly outperformed CNN-MRF with an average F1macro score of 97.6 % and ACC of 98.9 %. Results demonstrated the high potential of hybrid convolutional models with learning transfer to develop tools for automatic disease identification in crops of agricultural interest.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Fragaria spp.]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[detección de enfermedades]]></kwd>
<kwd lng="es"><![CDATA[reconocimiento de patrones]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales híbridas]]></kwd>
<kwd lng="es"><![CDATA[transferencia de aprendizaje]]></kwd>
<kwd lng="en"><![CDATA[Fragaria spp]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[disease detection]]></kwd>
<kwd lng="en"><![CDATA[hybrid neural networks]]></kwd>
<kwd lng="en"><![CDATA[learning transfer]]></kwd>
<kwd lng="en"><![CDATA[pattern recognition]]></kwd>
</kwd-group>
</article-meta>
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