Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática
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.
Palabras clave
algoritmos de predicción, aprendizaje de máquina, análisis químico, espectroscopía
Biografía del autor/a
Mateo Vargas-Zapata
Zootecnista
Marisol Medina-Sierra
Ingeniera Agrónoma, Doctora en Ciencias
Luis Fernando Galeano-Vasco
Zootecnista, Doctor en Ciencias
Mario Fernando Cerón-Muñoz
Zootecnista, Doctor en Ciencias
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