<?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>S0187-73802024000100062</article-id>
<article-id pub-id-type="doi">10.35196/rfm.2024.1.62</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Algoritmos de aprendizaje automático para identificar variedades de durazno con base en descriptores cromáticos y morfológicos]]></article-title>
<article-title xml:lang="en"><![CDATA[Machine learning algorithms to identify peach varieties based on chromatic and morphological descriptors]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ayala-Niño]]></surname>
<given-names><![CDATA[Daniel]]></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[Montecillo Estado de México]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2024</year>
</pub-date>
<volume>47</volume>
<numero>1</numero>
<fpage>62</fpage>
<lpage>69</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-73802024000100062&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-73802024000100062&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-73802024000100062&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La inteligencia artificial ha permitido desarrollar herramientas para el reconocimiento automático de frutos y hortalizas con mayor precisión y rapidez. El desarrollo de nuevos genotipos de árboles frutales requiere del uso de herramientas tecnológicas para la identificación de variedades con mayor robustez que los métodos convencionales. En esta investigación se aplicaron algoritmos de aprendizaje automático para identificar seis variedades de durazno (Prunus persica L.) CP-03-06, Oro Azteca, Oro San Juan, Cardenal, Colegio y Robin a partir de imágenes digitales de hojas. Los modelos máquina de soporte vectorial (SVM), bosque aleatorio (RF) y perceptrón multicapa (MLP) se entrenaron y evaluaron con base en tres descriptores cromáticos y 14 morfológicos extraídos de imágenes digitales. La evaluación del desempeño en predicción de los modelos se realizó con base en métricas globales y específicas para cada clase objetivo (variedad de durazno). Los cinco descriptores más importantes para identificar las variedades de durazno fueron los tres canales de color HSV (hue, saturation, value), la redondez y la excentricidad de las hojas. SVM obtuvo la mayor precisión global de clasificación con Acc de 98.7 % y F1macro de 98 %. SVM obtuvo el mayor puntaje F1 (99.2 %) para identificar la variedad de durazno CP-03-06 y el menor puntaje F1 (96.1 %) para identificar la variedad Cardenal. La utilización conjunta de descriptores cromáticos y morfológicos mejoró el desempeño de los algoritmos de aprendizaje para identificar las seis variedades de durazno. Los modelos SVM, RF y MLP obtuvieron un Acc de 98.7, 98.6 y 97 %, respectivamente. Este estudio muestra el potencial de las técnicas de aprendizaje automático para su aplicación en el reconocimiento de descriptores de interés en cultivos agrícolas y su aplicación a procesos automatizados en la agricultura.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary Artificial intelligence has allowed the development of tools for automatic recognition of fruits and vegetables with greater precision and speed. The development of new genotypes of fruit trees requires the use of technological tools to identify varieties with greater robustness than conventional methods. In this research, machine learning algorithms were applied to identify six peach varieties (Prunus persica L.) CP-03-06, Oro Azteca, Oro San Juan, Cardenal, Colegio and Robin from digital leaf images. The support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) models were trained and evaluated based on three chromatic and 14 morphological descriptors extracted from digital images. The evaluation of the prediction performance of the models was based on global metrics and specific for each target class (peach variety). The five most important descriptors to identify peach varieties were three HSV color channels (hue, saturation, value), roundness and eccentricity of the leaves. SVM achieved the highest overall classification accuracy with Acc of 98.7 % and F1macro of 98 %. SVM obtained the highest F1 score (99.2 %) to identify the peach variety CP-03-06 and the lowest F1 score (96.1 %) to identify the Cardenal variety. The joint use of chromatic and morphological descriptors improved the performance of learning algorithms to identify the six peach varieties. The SVM, RF and MLP models obtained an Acc of 98.7, 98.6 and 97 %, respectively. This study shows the potential of machine learning methods for their application in recognizing descriptors of interest in agricultural crops and their application to automated processes in agriculture.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Prunus persica L.]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[reconocimiento de patrones]]></kwd>
<kwd lng="es"><![CDATA[visión computacional]]></kwd>
<kwd lng="en"><![CDATA[Prunus persica L.]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[computer vision]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[pattern recognition]]></kwd>
</kwd-group>
</article-meta>
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