Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática

Machine learning algorithms for prediction of physicochemical soil properties by spectral information: a systematic review

Contenido principal del artículo

Mateo Vargas-Zapata
Marisol Medina-Sierra
Luis Fernando Galeano-Vasco
Mario Fernando Cerón-Muñoz

Resumen

En la literatura científica actual se discute ampliamente acerca de la predicción de propiedades edáficas mediante información espectral. El objetivo de esta revisión fue encontrar algoritmos con el mayor potencial predictivo para las propiedades fisicoquímicas del suelo, basados en información espectral capturada con diferentes instrumentos. Se realizó una revisión sistemática en la cual se encontraron 121 artículos de los cuales se eligieron 19, que cumplieran con un coeficiente de determinación mayor a 0,80 o una raíz del error cuadrado medio cercana a 0. Se determinó que el rango espectral más utilizado corresponde al rango desde 350 hasta 2500 nm; los algoritmos mínimos cuadrados parciales, máquina de soporte vectorial y máquina de soporte vectorial ajustado son adecuadas para predecir pH, materia orgánica y carbono orgánico. Además, la regresión lineal solo es efectiva para predecir el carbonato de calcio, materia orgánica, humedad y contenido de agua mediante bandas individuales.

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Biografía del autor/a (VER)

Mateo Vargas-Zapata, Universidad de Antioquia, Medellín

Zootecnista

Marisol Medina-Sierra, Universidad de Antioquia, Medellín

Ingeniera Agrónoma, Doctora en Ciencias

Luis Fernando Galeano-Vasco, Universidad de Antioquia, Medellín

Zootecnista, Doctor en Ciencias

Mario Fernando Cerón-Muñoz, Universidad de Antioquia, Medellín

Zootecnista, Doctor en Ciencias

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