Examinando por Autor "Macas Amaya, Esteban Nicolás"
Mostrando 1 - 1 de 1
Resultados por página
Opciones de ordenación
- ÍtemRestringidoA New approach to predict nutrient content in Costa Rican soils using spectroscopy and machine-learning.(Universidad EARTH, 2019-12) Cuarezma Espinoza, Katherine Michelle; Macas Amaya, Esteban Nicolás; Perret, Johan; Villalobos Leandro, José Eduardo; Cécillon, LauricReducing the cost and time of the soil analysis process becomes a priority in order to be more efficient day by day. That is one of the reasons why the use of visible and near infrared (vis-NIR) spectroscopy along with the application of chemometric models for nutrient prediction becomes important. To develop this study, a total of 1634 samples were collected. Parameters such as macronutrients (P, K, S, Ca, Mg) and micronutrients (Zn, Cu, Fe, Mn, B) and other elements for example Si and Na were analyzed. Different data preprocessing techniques were applied in addition to three different chemometric models: Random Forest, Partial Least Square Regression and Memory-Based Learning. Of a total of 19 processed variables, they resulted nine models with R2 values greater than 0.8 and an RPD greater than 2. In comparison between the models, memory-based learning obtained the highest coefficients for the prediction of nutrient content in agricultural soils in Costa Rica. Iron was the nutrient with the greatest predictive capacity with an R2 of 0.92 and an RPD of 3.62. Our results confirm that vis-NIR gives a good confidence in the prediction of some soil parameters.