Predicción de la fertilidad del suelo mediante aprendizaje automático en la provincia de Alto Amazonas, Perú
DOI:
https://doi.org/10.56926/repia.v3i2.63Palabras clave:
Random Forest, suelos amazónicos, modelamiento de suelos, suelos ácidosResumen
El objetivo del trabajo fue predecir la fertilidad del suelo en la provincia de Alto Amazonas con el uso de imágenes satelitales y técnicas de aprendizaje automático. El estudio se ubicó en la provincia de Alto Amazonas en Perú. Se realizaron muestreos de suelos en toda la provincia, totalizando 100 muestras. Posteriormente se realizaron análisis físicos (textura) y químicos del suelo. Las imágenes satelitales se obtuvieron del USGS y los índices de vegetación se calcularon con base en estas imágenes. Finalmente, se utilizó análisis descriptivo y modelado de aprendizaje automático utilizando 06 algoritmos (GLM, CUBIST, KKNN, SVM, Random Forest y NN) que se seleccionaron en función de su R2 y RMSE. En este trabajo observamos que la mayoría de los suelos de la provincia tienen bajos pH, P, Mg, K y alta acidez. También se lograron obtener buenas predicciones para pH, Ca, Mg y CIC y se observó que el algoritmo más exitoso fue Random Forest. Sin embargo, para Al, Cubist tuvo mejores resultados. Este es uno de los primeros trabajos que utiliza aprendizaje automático para predecir la fertilidad del suelo en la Amazonía peruana y se espera que pueda servir como base para futuros proyectos.
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Derechos de autor 2024 César Oswaldo Arévalo-Hernández, Enrique Arévalo-Gardini, Luis Alberto Arévalo-López, Oscar Tuesta-Hidalgo, Dayani Shirley Romero-Vela, Claudia Elizabeth Ruiz-Camus
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