<?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>1027-152X</journal-id>
<journal-title><![CDATA[Revista Chapingo. Serie horticultura]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Chapingo Ser.Hortic]]></abbrev-journal-title>
<issn>1027-152X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma Chapingo]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1027-152X2023000300115</article-id>
<article-id pub-id-type="doi">10.5154/r.rchsh.2023.04.002</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Automatic identification of avocado fruit diseases based on machine learning and chromatic descriptors]]></article-title>
<article-title xml:lang="es"><![CDATA[Identificación automática de enfermedades en frutos de aguacate con base en máquinas de aprendizaje y descriptores cromáticos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Campos-Ferreira]]></surname>
<given-names><![CDATA[Ulises Enrique]]></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 contrib-type="author">
<name>
<surname><![CDATA[Carrillo-Salazar]]></surname>
<given-names><![CDATA[Alfredo]]></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>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>29</volume>
<numero>3</numero>
<fpage>115</fpage>
<lpage>130</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1027-152X2023000300115&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1027-152X2023000300115&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1027-152X2023000300115&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Timely identification of phytosanitary problems in agricultural crops is essential to reduce production losses. Artificial intelligence algorithms facilitate their rapid and reliable identification. In this research, three learning classifiers, namely random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were evaluated to identify three target classes (healthy fruit, anthracnose [Colletotrichum spp.] and scab [Sphaceloma perseae]) from digital fruit images. Two color descriptor extraction techniques (region selection and image subsampling) were compared with the RF classifier, and an overall classification accuracy (ACC) of 98±0.03 % with region selection and 84±0.08 % with subsampling was obtained. Subsequently, the classifiers were evaluated with color descriptors extracted with region selection. RF and MLP were superior to SVM, with an ACC of 98±0.03 %. Scab and anthracnose were identified with an F1 score of 98 %. The high performance of the classifiers shows the potential for applying artificial intelligence paradigms to identify phytosanitary problems in agricultural crops.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La identificación oportuna de problemas fitosanitarios en cultivos agrícolas es esencial para reducir pérdidas de producción. Los algoritmos de inteligencia artificial facilitan su identificación rápida y confiable. En esta investigación, se evaluaron tres clasificadores de aprendizaje: bosque aleatorio (RF), máquina de soporte vectorial (SVM) y perceptrón multicapa (MLP), para identificar tres clases objetivo (frutos sanos, antracnosis [Colletotrichum spp.] y roña [Sphaceloma perseae]) a partir de imágenes digitales de frutos. Se compararon dos técnicas de extracción de descriptores de color (selección por región y submuestreo de imágenes) con el clasificador RF, y se obtuvo una precisión global de clasificación (ACC) de 98±0.03 % con selección por región, y de 84±0.08 % con submuestreo. Posteriormente, los clasificadores se evaluaron con descriptores de color extraídos con selección por región. RF y MLP fueron superiores a SVM, con una ACC de 98±0.03 %. La roña y la antracnosis se identificaron con un puntaje F1 de 98 %. El alto desempeño de los clasificadores muestra el potencial de aplicación de los paradigmas de inteligencia artificial para identificar problemas fitosanitarios en cultivos agrícolas.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Persea americana]]></kwd>
<kwd lng="en"><![CDATA[Sphaceloma perseae]]></kwd>
<kwd lng="en"><![CDATA[Colletotrichum spp.]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence.]]></kwd>
<kwd lng="es"><![CDATA[Persea americana]]></kwd>
<kwd lng="es"><![CDATA[Sphaceloma perseae]]></kwd>
<kwd lng="es"><![CDATA[Colletotrichum spp.]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial.]]></kwd>
</kwd-group>
</article-meta>
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