Evaluación de la Eficacia de Modelos de Machine Learning para la toma de decisiones basado en Datos Meteorológicos en la producción agrícola.

Nahomi Machuca; Marlon Renne Navia Mendoza; Luis Cedeño Valarezo

doi:10.46480/esj.9.2.232

Authors

Nahomi Machuca Universidad Laica Eloy Alfaro de Manabí
Marlon Renne Navia Mendoza Universidad Técnica de Manabí
Luis Cedeño Valarezo Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, Carrera de Cumputación, Grupo SISCOM

DOI:

https://doi.org/10.46480/esj.9.2.232

Keywords:

agricultural prediction, predictive models, climate, Gradient Boosting, Random Forest

Abstract

Weather is one of the factors that most often influences agricultural production. This study evaluates the effectiveness of Machine Learning models in predicting agricultural yield based on meteorological data. A methodology based on OSEMN (Obtain, Scrub, Explore, Model, Interpret) was used for data processing and model construction, evaluating their performance and prediction accuracy through metric statistics. Models based on Gradient Boosting, Random Forest, XGBoost, and SVM techniques were evaluated. The results showed that the XGBoost model obtained the best predictive performance in terms of accuracy (MAE = 0.1104, RMSE = 0.1367), followed by Gradient Boosting (MAE = 0.1104, RMSE = 0.1370), while SVM presented the lowest predictive capacity in this context (MAE = 0.1223, RMSE = 0.1529). These findings highlight the potential of decision tree-based approaches, enabling anticipation of production changes and improved planning based on weather conditions.

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