<?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>0121-1129</journal-id>
<journal-title><![CDATA[Revista Facultad de Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Fac. ing.]]></abbrev-journal-title>
<issn>0121-1129</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-11292020000100018</article-id>
<article-id pub-id-type="doi">10.19053/01211129.v29.n54.2020.10853</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia]]></article-title>
<article-title xml:lang="es"><![CDATA[Comparación de modelos de aprendizaje automático para la predicción de rendimientos agrícolas en cultivos de cacao en Santander, Colombia]]></article-title>
<article-title xml:lang="pt"><![CDATA[Comparação de modelos de aprendizado de máquina para a previsão de produção agrícola em cacau em Santander, Colômbia]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lamos-Díaz]]></surname>
<given-names><![CDATA[Henry]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Puentes-Garzón]]></surname>
<given-names><![CDATA[David-Esteban]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zarate-Caicedo]]></surname>
<given-names><![CDATA[Diego-Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Industrial de Santander  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Industrial de Santander  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Corporación Colombiana de Investigación Agropecuaria  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2020</year>
</pub-date>
<volume>29</volume>
<numero>54</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-11292020000100018&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-11292020000100018&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-11292020000100018&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La identificación de los factores que influyen en el rendimiento (kg·ha-1) de un cultivo provee información esencial para la toma de decisiones orientadas al mejoramiento y predicción de la productividad, proporcionando posibilidades a los agricultores para mejorar sus ingresos económicos. En este estudio, se presenta la aplicación y comparación de diversos algoritmos de aprendizaje automático para la predicción del rendimiento agrícola en cultivos de cacao y la identificación de los factores que influyen sobre éste. Se comparan los algoritmos de máquinas de soporte vectorial (SVM), modelos ensamblados (Random Forest, Gradient Boosting) y el modelo de regresión Least Absolute Shrinkage and Selection Operator (LASSO). Los predictores considerados fueron: condiciones climáticas de la región, variedad de cacao, nivel de fertilización y exposición al sol para un cultivo experimental ubicado en Rionegro, Santander. Los resultados identifican a Gradient Boosting como la mejor alternativa de pronóstico con un coeficiente de determinación (R2) = 68 %, Error Absoluto Medio (MAE) = 13.32 y Raíz Cuadrada del Error Medio (RMSE) = 20.41. La variabilidad del rendimiento del cultivo es explicada principalmente por la radiación y la temperatura un mes previo a la cosecha, además de las lluvias acumuladas el mes de la cosecha. De igual manera, los rendimientos de los cultivos son evaluados con base en el tipo de exposición al sol, encontrando que la radiación un mes previo a la cosecha es el factor más influyente para los cultivos bajo sombra. Por otro lado, la lluvia y la humedad son las variables determinantes en las plantas con exposición plena a sol, lo que está asociado a los requerimientos hídricos. Estos resultados sugieren un manejo diferenciado de los cultivos dependiendo del tipo de exposición, sin tener que comprometer la productividad, dado que no se evidencia diferencia significativa en los rendimientos de ambos manejos agrícolas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A identificação de fatores que influenciam o rendimento (kg·ha-1) de uma safra fornece informações essenciais para a tomada de decisões com o objetivo de melhorar e prever a produtividade, oferecendo possibilidades aos agricultores de melhorar sua renda econômica. Neste estudo, são apresentadas a aplicação e comparação de vários algoritmos de aprendizado de máquina para a previsão do desempenho agrícola em cultivos de cacau e a identificação dos fatores que o influenciam. Os algoritmos de máquinas de suporte de vetores (SVM), modelos montados (floresta aleatória, reforço de gradiente) e o modelo de regressão Operador de seleção e contração mínimos absolutos (LASSO) são comparados. Os preditores considerados foram: condições climáticas da região, variedade de cacau, nível de fertilização e exposição ao sol para uma cultura experimental localizada em Rionegro, Santander. Os resultados identificam o Gradient Boosting como a melhor alternativa de prognóstico com um coeficiente de determinação (R2) = 68%, Erro Absoluto Médio (MAE) = 13.32 e Erro Médio de Raiz Quadrada (RMSE) = 20.41. A variabilidade do rendimento das culturas é explicada principalmente pela radiação e temperatura um mês antes da colheita, além das chuvas acumuladas no mês da colheita. Da mesma forma, os rendimentos das culturas são avaliados com base no tipo de exposição ao sol, constatando que a radiação um mês antes da colheita é o fator mais influente para as culturas sombreadas. Por outro lado, chuva e umidade são as variáveis determinantes em plantas com exposição solar total, as quais estão associadas às necessidades de água. Esses resultados sugerem um manejo diferenciado das culturas, dependendo do tipo de exposição, sem comprometer a produtividade, uma vez que não há diferença significativa nos rendimentos de ambos os manejos agrícolas.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[agricultural yield]]></kwd>
<kwd lng="en"><![CDATA[agroforestry system]]></kwd>
<kwd lng="en"><![CDATA[cocoa]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="en"><![CDATA[productivity]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[cacao]]></kwd>
<kwd lng="es"><![CDATA[predicción]]></kwd>
<kwd lng="es"><![CDATA[productividad]]></kwd>
<kwd lng="es"><![CDATA[rendimientos agrícolas]]></kwd>
<kwd lng="es"><![CDATA[sistemas agroforestales]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado de máquina]]></kwd>
<kwd lng="pt"><![CDATA[cacau]]></kwd>
<kwd lng="pt"><![CDATA[predição]]></kwd>
<kwd lng="pt"><![CDATA[produtividade]]></kwd>
<kwd lng="pt"><![CDATA[rendimentos agrícolas]]></kwd>
<kwd lng="pt"><![CDATA[sistemas agroflorestais]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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