<?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>2007-9028</journal-id>
<journal-title><![CDATA[Ecosistemas y recursos agropecuarios]]></journal-title>
<abbrev-journal-title><![CDATA[Ecosistemas y recur. agropecuarios]]></abbrev-journal-title>
<issn>2007-9028</issn>
<publisher>
<publisher-name><![CDATA[Universidad Juárez Autónoma de Tabasco, Dirección de Investigación y Posgrado]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-90282024000400022</article-id>
<article-id pub-id-type="doi">10.19136/era.a11niv.4130</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Identificación de defectos de calidad en frutos de aguacate &#8216;Hass&#8217; mediante procesamiento de imágenes]]></article-title>
<article-title xml:lang="en"><![CDATA[Identification of Quality Defects in 'Hass' Avocado Fruits Using Image Processing]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[León-Bañuelos]]></surname>
<given-names><![CDATA[Luis Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-Martínez]]></surname>
<given-names><![CDATA[René]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Vázquez]]></surname>
<given-names><![CDATA[Gabriela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico Nacional de México  ]]></institution>
<addr-line><![CDATA[Valle de Bravo Estado de México]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>11</volume>
<numero>spe4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-90282024000400022&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-90282024000400022&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-90282024000400022&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen México, es el principal productor de aguacate &#8216;Hass&#8217; (Persea americana Mill.) a nivel mundial. Sin embargo, los pequeños productores suelen presentar problemas de calidad y no cumplen con estándares de la norma NMX-FF-016-2002. Por cual, el objetivo de la investigación fue identificar defectos de calidad en frutos mediante procesamiento de imágenes. Para ello, en septiembre de 2023 se cosecharon frutos en madurez fisiológica en diversos huertos de la región de Valle de Bravo, México. A cada aguacate se le midió el diámetro, longitud y peso. La evaluación de defectos de calidad se realizó por un experto y, simultáneamente, se tomaron fotografías para analizar las imágenes mediante algoritmos de binarización y Canny con la finalidad de identificar daños físicos externos y estimar la longitud, diámetro y peso de cada fruto. Se aplicó una prueba de t para comparar el tamaño del fruto estimado por el algoritmo y datos reales. Como resultado se observaron diferencias significativas (p &lt; 0.01) en longitud y diámetro, por lo cual, se aplicó un factor de ajuste para optimizar los resultados del algoritmo. Se obtuvo un modelo de regresión lineal múltiple (p &lt; 0.01, r2 = 0.91) que estima el peso en función del diámetro y longitud. El proceso de binarización ayudó a identificar daños por trips, raspaduras y roña. Mientras que con el método de Canny se identificaron daños por trips y roña. En conclusión, el análisis de imágenes fue eficiente para identificar defectos causados por roña, trips y raspaduras, así como la estimación del tamaño de los frutos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Mexico is the main producer of 'Hass' avocado (Persea Americana Mill.) worldwide. However, small producers often present quality problems and do not meet the standards of the NMX-FF-016-2002 standard. Therefore, the objective of the research was to identify quality defects in fruits through image processing. To do this, in September 2023, fruits at physiological maturity were harvested in various orchards in the Valle de Bravo region, Mexico. The diameter, length and weight of each avocado were measured. The evaluation of quality defects was carried out by an expert and, simultaneously, photographs were taken to analyze the images using binarization and Canny algorithms to identify external physical damage and estimate the length, diameter and weight of each fruit. A t test was applied to compare the fruit size estimated by the algorithm and real data. As a result, significant differences (p &lt; 0.01) were observed in length and diameter, therefore, an adjustment factor was applied to optimize the results of the algorithm. A multiple linear regression model was obtained (p &lt; 0.01, r2 = 0.91) that estimates weight as a function of diameter and length. The binarization process helped identify damage from thrips, scrapes, and scab. While with the Canny method, damage from thrips and scab was identified. In conclusion, image analysis was efficient in identifying defects caused by scabs, thrips and scratches, as well as estimating fruit size.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Análisis de imágenes]]></kwd>
<kwd lng="es"><![CDATA[visión por computadora]]></kwd>
<kwd lng="es"><![CDATA[enfermedades en aguacate]]></kwd>
<kwd lng="es"><![CDATA[agricultura de precisión]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="en"><![CDATA[Image analysis]]></kwd>
<kwd lng="en"><![CDATA[computer vision]]></kwd>
<kwd lng="en"><![CDATA[avocado diseases]]></kwd>
<kwd lng="en"><![CDATA[precision agriculture]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
</kwd-group>
</article-meta>
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