<?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>2007-9753</journal-id>
<journal-title><![CDATA[RIIIT. Revista internacional de investigación e innovación tecnológica]]></journal-title>
<abbrev-journal-title><![CDATA[RIIIT. Rev. int. investig. innov. tecnol.]]></abbrev-journal-title>
<issn>2007-9753</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma de Coahuila]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-97532023000500049</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Niveles de salinidad del suelo, utilizando imágenes de vehículos aéreos no tripulados, redes neuronales y árboles de decisión]]></article-title>
<article-title xml:lang="en"><![CDATA[Soil salinity levels, using unmanned aerial vehicles imagery, neural networks and decision trees]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Santoyo-de la Cruz]]></surname>
<given-names><![CDATA[M.F.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Flores-Magdaleno]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gardezi]]></surname>
<given-names><![CDATA[A.K.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rubiños-Panta]]></surname>
<given-names><![CDATA[J.E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mancilla-Villa]]></surname>
<given-names><![CDATA[O.R.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados Departamento de Hidrociencias ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Guadalajara Centro Universitario de la Costa Sur Departamento de Producción Agrícola]]></institution>
<addr-line><![CDATA[Autlán Jalisco]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>10</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>10</month>
<year>2023</year>
</pub-date>
<volume>11</volume>
<numero>64</numero>
<fpage>49</fpage>
<lpage>65</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-97532023000500049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-97532023000500049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-97532023000500049&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La salinidad del suelo es un problema mundial que amenaza el crecimiento y rendimiento de los cultivos e impide el desarrollo sostenible de la agricultura moderna, los principales cultivos básicos no pueden completar su ciclo de vida cuando las concentraciones de NaCl en el suelo superan los 200 mM. Más de un tercio de las parcelas de riego del mundo están afectadas por la salinización. Los vehículos aéreos no tripulados ofrecen una alternativa viable para adquirir datos de teledetección. Una manera para clasificar y detectar variables de salinidad en el suelo son las redes neuronales y los árboles de decisión. El objetivo de este trabajo es la detección y clasificación de la salinidad del suelo mediante el uso de imágenes multiespectrales capturadas mediante drones, así como el uso de modelos de árboles de decisión y redes neuronales, para estimar variables de salinidad: Porciento de Sodio Intercambiable (PSI), Relación de Adsorción de Sodio ajustado (RASaj) y concentración de sodio (mEq L-1). Se muestreó suelo agrícola con problemas de salinidad, se procesaron en laboratorio, los resultados se interpretaron y se clasificaron de acuerdo a sus niveles de salinidad. Se realizó un sobrevuelo con un dron que capturó imágenes y se extrajeron las reflectancias de las cuatro bandas multiespectrales (Green, red, red edge y near infrared), se calcularon los índices de salinidad para clasificar la salinidad del suelo usando redes neuronales y árboles de decisión. Las redes neuronales clasificaron el 82.6% de las muestras en la categoría de nivel medio en la estimación de PSI. El modelo también presentó una precisión del 71.88% cuando se determinó la concentración de sodio en la red neuronal. El trabajo de investigación demostró que es posible clasificar la salinidad del suelo con aceptable precisión a partir de las imágenes capturadas con vehículos aéreos no tripulados. Los modelos de redes neuronales mostraron mejor precisión en la estimación de los indicadores de salinidad del suelo con respecto a los árboles de decisión, estos procedimientos tecnológicos pueden aplicarse en el mapeo de la salinidad del suelo para conocer las áreas afectadas de una manera fácil y rápida.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Soil salinity is a global problem that threatens crop growth, and yields and impedes the and sustainable development of modern agriculture with major staple crops unable to complete their life cycle when soil NaCl concentrations exceed 200 mM. More than one third of the world's irrigated plots are affected by salinization. Unmanned aerial vehicles offer a viable alternative for acquiring remote sensing data. One way to classify and detect soil salinity variables are neural networks and decision trees. The objective of this work is the detection and classification of soil salinity through the use of multispectral images captured by drones as well as the use of decision tree models and neural networks, to estimate salinity variables: Exchangeable Sodium Percentage (ESP), Adjusted Sodium Adsorption Ratio (SARaj) and sodium concentration (mEq L-1). Agricultural soil with salinity problems was sampled, processed in the laboratory, the results were interpreted and classified according to salinity levels. A drone overflight was conducted to captured images and extract reflectances of the four multispectral bands (Green, red, red edge and near infrared). The salinity indexes were calculated to classify soil salinity using neural networks and decision trees. The neural networks classified 82.6% of the samples in the medium level category of ESP estimation. The model also presented an accuracy of 71.88% when determining sodium ion concentration in the neural networks. The Research work showed that it is possible to classify soil salinity with acceptable precision from images captured with unmanned aerial vehicles. Neural network models showed better accuracy in the estimation of soil salinity indicators with respect to decision trees. These technological procedures can be applied in soil salinity mapping to know the affected areas in an easy and fast way.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[agricultura de precisión]]></kwd>
<kwd lng="es"><![CDATA[drones]]></kwd>
<kwd lng="es"><![CDATA[imágenes multiespectrales]]></kwd>
<kwd lng="es"><![CDATA[índices de salinidad]]></kwd>
<kwd lng="es"><![CDATA[reflectancias]]></kwd>
<kwd lng="en"><![CDATA[drones]]></kwd>
<kwd lng="en"><![CDATA[multispectral images]]></kwd>
<kwd lng="en"><![CDATA[precision agriculture]]></kwd>
<kwd lng="en"><![CDATA[reflectances]]></kwd>
<kwd lng="en"><![CDATA[salinity index]]></kwd>
</kwd-group>
</article-meta>
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