Predicción del rendimiento de cultivos de café: un mapeo sistemático
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El café es uno de los productos agrícolas más comercializados internacionalmente y en Colombia, es el primer producto de exportación no minero-energético. En este contexto, la predicción del rendimiento de los cultivos de café es vital para el sector, ya que permite a los caficultores establecer estrategias de manejo del cultivo, maximizando sus ganancias o reduciendo posibles pérdidas. En este artículo, se abordan aspectos cruciales de la predicción del rendimiento de los cultivos de café mediante una revisión sistemática de literatura de documentos consultados en Scopus, ACM, Taylor & Francis y Nature. Estos documentos se sometieron a un proceso de filtrado y evaluación para responder cinco preguntas clave: variables predictoras, variable objetivo, técnicas y algoritmos empleados, métricas para evaluar la calidad de la predicción y tipos de café reportados. Los resultados revelan distintos grupos de variables predictoras que incluyen factores atmosféricos, químicos, obtenidos vía satélite, relacionados con fertilizantes, suelo, manejo del cultivo y sombras. La variable objetivo más recurrente es el rendimiento medido en peso de granos por hectárea u otras medidas, con un caso que considera el área foliar. Entre las técnicas predominantes para predecir el rendimiento se encuentran la regresión lineal, los bosques aleatorizados, el análisis de componentes principales, la regresión por conglomerados, las redes neuronales, los árboles de clasificación y regresión y las máquinas de aprendizaje extremo, entre otras. Las métricas más comunes para evaluar la calidad de los modelos predictivos incluyen R², RMSE, MAE, MAPE, MRE, error estándar, coeficiente de correlación de Pearson y desviación estándar. Por último, las variedades de café más estudiadas son robusta, arábica, racemosa y zanguebariae.
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Aceptado 2023-08-23
Publicado 2023-09-20
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