<?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>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822022000400116</article-id>
<article-id pub-id-type="doi">10.18845/tm.v35i4.5777</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Plantlia: free android app for measurement of foliar area and color with automated scaling]]></article-title>
<article-title xml:lang="es"><![CDATA[Plantlia: app gratuita de Android para medición de área foliar y color con escalamiento automático]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valerio]]></surname>
<given-names><![CDATA[Ovidio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Plarepi  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>35</volume>
<numero>4</numero>
<fpage>116</fpage>
<lpage>123</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822022000400116&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822022000400116&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822022000400116&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract There is a plethora of apps available for different biological applications, however, mobile software for plant measurement, specifically for field conditions are limited. Additionally, there is a need to create large training datasets for machine learning applications. A free app called Plantlia, which is available to download from the Google Play Store, aims to resolve this with an intuitive interface, and a method to automatically scale images using homography. Plantlia also includes methods to share results from either direct measurements or thresholded images. This paper aims to describe some of the functions of Plantlia, as well as show scenarios that display its performance. Images from cellphones and drone pictures were used for validation on different devices. This is important, as it means that users with low-cost equipment, like drones with no GPS information, can still analyze localized field information. Similarly, researchers can do communal efforts to share and receive in-field data to create machine learning datasets.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Hay una gran cantidad de apps disponibles para diferentes estudios biológicos, sin embargo, aplicaciones mobiles para la medición de plantas, específicamente para condiciones de campo, son limitadas. Además, existe la necesidad de crear grandes conjuntos de datos de entrenamiento para aplicaciones de aprendizaje automático. Una aplicación gratuita llamada Plantlia, que está disponible para descargar desde Google Play Store, tiene como objetivo resolver esto con una interfaz intuitiva y un método para escalar imágenes automáticamente usando homografía. Plantlia también incluye métodos para compartir resultados de mediciones directas o imágenes segmentadas. Este artículo tiene como objetivo describir algunas de las funciones de Plantlia, así como mostrar escenarios que muestran su desempeño. Se utilizaron imágenes de dispositivos móviles y de drones para su validación en diferentes dispositivos. Esto es importante, ya que significa que los usuarios con equipos de bajo costo, como drones sin información de GPS, pueden analizar información de campo localizada. De manera similar, los investigadores pueden hacer esfuerzos colectivos para compartir y recibir datos de campo para crear conjuntos de datos para aprendizaje automático.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Procesamiento de imágenes]]></kwd>
<kwd lng="es"><![CDATA[Android]]></kwd>
<kwd lng="es"><![CDATA[área foliar]]></kwd>
<kwd lng="es"><![CDATA[escalamiento automático]]></kwd>
<kwd lng="en"><![CDATA[Image processing]]></kwd>
<kwd lng="en"><![CDATA[Android]]></kwd>
<kwd lng="en"><![CDATA[foliar area]]></kwd>
<kwd lng="en"><![CDATA[automatic scaling]]></kwd>
</kwd-group>
</article-meta>
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