<?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>2730-5066</journal-id>
<journal-title><![CDATA[Agrociencia Uruguay]]></journal-title>
<abbrev-journal-title><![CDATA[Agrocienc. Urug.]]></abbrev-journal-title>
<issn>2730-5066</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Agronomía - Instituto de Nacional de Investigación Agropecuaria]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2730-50662025000101315</article-id>
<article-id pub-id-type="doi">10.31285/agro.29.1530</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[High-throughput phenotyping using aerial images for predicting agronomic traits in soybean breeding programs]]></article-title>
<article-title xml:lang="es"><![CDATA[Fenotipado de alto rendimiento usando imágenes aéreas para predecir rasgos agronómicos en programas de mejoramiento de soja]]></article-title>
<article-title xml:lang="pt"><![CDATA[Fenotipagem de alto rendimento usando imagens aéreas para prever características agronômicas em programas de melhoramento de soja]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez]]></surname>
<given-names><![CDATA[O. M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Investigación Agropecuaria (INIA) Sistema Agrícola-Ganadero Área de Ecofisiología y Manejo de Cultivos]]></institution>
<addr-line><![CDATA[Canelones ]]></addr-line>
<country>Uruguay</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>29</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.edu.uy/scielo.php?script=sci_arttext&amp;pid=S2730-50662025000101315&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.edu.uy/scielo.php?script=sci_abstract&amp;pid=S2730-50662025000101315&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.edu.uy/scielo.php?script=sci_pdf&amp;pid=S2730-50662025000101315&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Plant breeding programs know the advantages of high-throughput phenotyping (HTP) in increasing efficiency over classical phenotyping and screening methods, which is achieved by saving time and improving selection accuracy. Even so, most programs have not yet systematically implemented this technology into their breeding pipelines. This review aims to indicate the restrictions of implementing HTP at a large scale and to summarize studies according to the used devices, data classes collected, and artificial intelligence (AI) methods applied to predict and classify agronomic traits in plant breeding programs with a focus on soybean (Glycine max (L.) Merr.). Excluding HTP platforms in laboratories and greenhouses, satellite remote sensing, and autonomous mobile robots, this review focuses on field-based HTP platforms that take aerial images from drones and apply AI methods to associate those images with the traits of interest. Field-based HTP research is also conducted using hand-held devices that record individual vegetation indices (e.g., NDVI), a few spectral bands (multispectral radiometers), or the continuous range of the electromagnetic light spectrum (spectroradiometers). However, plant breeders must evaluate thousands of experimental lines each year, so using these devices instead of drones implies a trade-off between acquisition accuracy and the time it takes to collect the data. A challenge in the coming years is fine-tuning scalable, reliable models and optimizing data input, processing, and output pipelines to provide breeders with helpful information before they make selections.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Los programas de fitomejoramiento conocen las ventajas del fenotipado de alto rendimiento (FAR) para incrementar la eficiencia de los métodos de fenotipado clásico y cribado, lo cual se logra ahorrando tiempo y mejorando la precisión de selección. Aun así, la mayoría de los programas todavía no han implementado sistemáticamente esta tecnología en sus líneas de mejoramiento. Esta revisión tiene por objetivo indicar restricciones de implementar FAR a gran escala y resumir estudios según dispositivos usados, clases de datos recolectados y métodos de inteligencia artificial (IA) aplicados para predecir y clasificar caracteres agronómicos en programas de mejoramiento vegetal con foco en soja (Glycine max (L.) Merr.). Excluyendo plataformas FAR en laboratorios e invernáculos, sensoramiento remoto satelital y robots móviles autónomos, esta revisión se enfoca en plataformas FAR a nivel de campo que toman imágenes aéreas desde drones y aplican métodos de IA para asociar esas imágenes con caracteres de interés. Investigaciones en FAR a nivel de campo también se realizan con dispositivos portátiles que registran índices de vegetación individuales (por ejemplo, NDVI), unas pocas bandas espectrales (radiómetros multiespectrales) o el rango continuo del espectro electromagnético de la luz (espectrorradiómetros). Sin embargo, los fitomejoradores deben evaluar miles de líneas experimentales cada año, por lo que usar estos dispositivos en vez de drones implica un compromiso entre precisión de adquisición y el tiempo que lleva colectar los datos. Un desafío en los próximos años es ajustar modelos escalables y confiables y optimizar canales de entrada, procesamiento y salida de datos para proveer información útil a los mejoradores antes de realizar las selecciones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo: Os programas de melhoramento de plantas conhecem as vantagens da fenotipagem de alto rendimento (FAR) para aumentar a eficiência dos métodos de fenotipagem clássico e triagem, o que é alcançado economizando tempo e melhorando a precisão da seleção. No entanto, a maioria dos programas ainda não utiliza sistematicamente essa tecnologia em suas linhas de melhoramento. Esta revisão tem como objetivo indicar as restrições de implementar FAR em larga escala e resumir estudos sobre dispositivos utilizados, tipos de dados coletados e métodos de inteligência artificial (IA) aplicados para prever e classificar características agronômicas em programas de melhoramento de plantas com foco na soja (Glycine max (L.) Merr.). Excluindo plataformas FAR em laboratórios e casas de vegetação, sensoriamento remoto via satélite e robôs móveis autônomos, esta revisão se concentra em plataformas FAR-de-campo que capturam imagens aéreas por drones e aplicam métodos de IA para associar essas imagens a características de interesse. A pesquisa FAR-de-campo também é conduzida com dispositivos portáteis que registram índices de vegetação individuais (e.g., NDVI), algumas bandas espectrais (radiômetros multiespectrais) ou o espectro eletromagnético contínuo da luz (espectrorradiômetros). No entanto, os fitomelhoristas devem avaliar milhares de linhagens experimentais a cada ano, pelo que a utilização destes dispositivos em vez de drones implica um obstáculo entre a precisão da aquisição e o tempo necessário para a obtenção de dados. Um desafio nos próximos anos será ajustar modelos escaláveis e confiáveis e otimizar canais de entrada, processamento e saída de dados para fornecer informações úteis aos melhoristas antes que façam as seleções.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[digital agriculture]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[phenomics]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
<kwd lng="en"><![CDATA[soybean]]></kwd>
<kwd lng="es"><![CDATA[agricultura digital]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[fenómica]]></kwd>
<kwd lng="es"><![CDATA[sensoramiento remoto]]></kwd>
<kwd lng="es"><![CDATA[soja]]></kwd>
<kwd lng="pt"><![CDATA[agricultura digital]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado automático]]></kwd>
<kwd lng="pt"><![CDATA[fenômica]]></kwd>
<kwd lng="pt"><![CDATA[sensoriamento remoto]]></kwd>
<kwd lng="pt"><![CDATA[soja]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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