<?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>0123-921X</journal-id>
<journal-title><![CDATA[Tecnura]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnura]]></abbrev-journal-title>
<issn>0123-921X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-921X2024000400075</article-id>
<article-id pub-id-type="doi">10.14483/22487638.22360</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Monitoreo de cultivos y suelos en agricultura de precisión con UAV e inteligencia artificial: una revisión]]></article-title>
<article-title xml:lang="en"><![CDATA[Crop and Soil Monitoring in Precision Agriculture with UAVs and Artificial Intelligence: A Review]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Buitrago Bolívar]]></surname>
<given-names><![CDATA[Elías]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rico Franco]]></surname>
<given-names><![CDATA[John Alexander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rojas Amador]]></surname>
<given-names><![CDATA[Sócrates]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Escuela Tecnológica Instituto Técnico Central Grupo de Investigación K-Demy ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Escuela Tecnológica Instituto Técnico Central  Grupo de Investigación K-Demy]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Escuela Tecnológica Instituto Técnico Central  Grupo de Investigación K-Demy]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>28</volume>
<numero>82</numero>
<fpage>75</fpage>
<lpage>103</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-921X2024000400075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-921X2024000400075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-921X2024000400075&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto: la creciente demanda global de alimentos, junto con los retos ambientales y sociales asociados a la inten sificación agrícola, ha impulsado el desarrollo de soluciones tecnológicas que mejoren la eficiencia y sostenibilidad de la producción. En este escenario, la agricultura de precisión, apoyada en vehículos aéreos no tripulados (unman ned aerial vehicle (UAV)) y en inteligencia artificial (IA), se posiciona como una herramienta clave para el monitoreo detallado de cultivos y suelos.  Objetivo:  este artículo presenta una revisión estructurada de la literatura científica sobre técnicas de detección remota basadas en UAV, con énfasis en aplicaciones orientadas a la estimación de niveles de fertilización, biomasa aérea, predicción de rendimiento y detección de plagas y malezas en sistemas agrícolas.  Metodología:  se efectuó una búsqueda sistemática en bases de datos académicas (Scopus y Web of Science), me diante combinaciones de términos clave relacionados con agricultura de precisión, UAV, teledetección, IA y moni toreo agronómico. Se recurrió a criterios de selección rigurosos que resultaron en la inclusión de 62 artículos para análisis. La información se sintetizó mediante un enfoque comparativo de técnicas, sensores, algoritmos y métricas de desempeño.  Resultados: la revisión evidenció una tendencia creciente hacia el uso de UAV equipados con sensores RGB, mul tiespectrales, hiperespectrales y LiDAR, junto con técnicas de aprendizaje automático y profundo, para estimar parámetros clave del cultivo como el índice de área foliar (leaf area index (LAI)), contenido de nitrógeno y rendi miento. Se identificaron enfoques prometedores basados en fusión multimodal de datos y modelos híbridos (CNN +GRU,ensambles), capaces de superar limitaciones de métodos clásicos como la saturación espectral. Sin embargo, se detectó escasa disponibilidad de bases de datos abiertas y poca estandarización en los protocolos de adquisición, lo que dificulta la replicabilidad y generalización de los modelos.  Conclusiones:  el uso integrado de UAV e IA representa una herramienta transformadora para la gestión agrícola inteligente. No obstante, su implementación efectiva requiere superar barreras técnicas, económicas y estructura les; además, debe promover el acceso abierto a datos y el desarrollo de soluciones contextualizadas. Esta revisión destaca la importancia de avanzar hacia sistemas más explicables, ligeros y adaptables, así como de fomentar una transformación digital agrícola inclusiva y responsable.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background: The growing global demand for food, along with the environmental and social challenges associated with agricultural intensification, has driven the development of technological solutions aimed at improving the efficiency and sustainability of food production. In this context, precision agriculture, supported by unmanned aerial vehicles (UAVs) and artificial intelligence (AI), emerges as a key tool for the detailed monitoring of crops and soils.  Objective: This article presents a structured review of the scientific literature on UAV-based remote sensing techniques, with an emphasis on applications aimed at estimating fertilization levels, aboveground biomass, yield prediction, and the detection of pests and weeds in agricultural systems.  Methodology: A systematic search was conducted in academic databases (Scopus and Web of Science), using combinations of key terms related to precision agriculture, UAV, remote sensing, AI, and agronomic monitoring. Rigorous inclusion criteria were applied, resulting in the selection of 62 articles for analysis. The information was synthesized through a comparative approach of techniques, sensors, algorithms, and performance metrics.  Results: The review highlights a growing trend in the use of UAVs equipped with RGB, multispectral, hyperspectral, and LiDAR sensors, combined with machine learning and deep learning techniques, to estimate key crop parameters such as leaf area index (LAI), nitrogen content, and yield. Promising approaches were identified based on multimodal data fusion and hybrid models (CNN + GRU, ensemble methods), capable of overcoming limitations of classical methods such as spectral saturation. However, a lack of open-access datasets and limited standardization in data acquisition protocols were observed, which hinders the replicability and generalization of models.  Conclusions: The integrated use of UAVs and AI represents a transformative tool for smart agricultural management. Nevertheless, effective implementation requires overcoming technical, economic, and structural barriers, as well as promoting open data access and the development of context-aware solutions. This review underscores the importance of advancing toward more explainable, lightweight, and adaptable systems, and fostering an inclusive and responsible digital transformation of agriculture.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[agricultura de precisión]]></kwd>
<kwd lng="es"><![CDATA[UAV]]></kwd>
<kwd lng="es"><![CDATA[detección remota]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[fertilización]]></kwd>
<kwd lng="es"><![CDATA[biomasa]]></kwd>
<kwd lng="es"><![CDATA[predicción de rendimiento]]></kwd>
<kwd lng="es"><![CDATA[apropiación tecnológica]]></kwd>
<kwd lng="en"><![CDATA[Precision agriculture]]></kwd>
<kwd lng="en"><![CDATA[UAV]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[fertilization]]></kwd>
<kwd lng="en"><![CDATA[biomass]]></kwd>
<kwd lng="en"><![CDATA[yield prediction]]></kwd>
<kwd lng="en"><![CDATA[technological adoption]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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