Cargando...
Fecha
2025-01-17
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Springer
Citas
Resumen
Agriculture continues to be one of the world’s main sources of income and provides great environmental, territorial and social value. However, frost is a recurring problem for farmers each year, representing a significant threat to agricultural production. In a matter of hours, temperatures below the freezing point can result in the loss of nearly the entire crop from a producer. In this article, we have analyzed and compared the application of a set of machine learning algorithms to predict the occurrence of frost events in the next 24 hours. The prediction process covers several challenges, such as data capture, processing, extracting each relevant parameter and finally building different prediction models to compared their performance. Furthermore, we have employed the Synthetic Minority Oversampling Technique (SMOTE) methodology to address the issue of imbalanced datasets, given the natural scarcity of frost events during the data sampling period. Our results show that among the machine learning algorithms we compared, the most efficient in terms of Recall score is K-Nearest Neighbor (KNN), while using the Area Under Curve (AUC) criteria, the highest score belongs to the Extra Trees algorithm, with 0.9909. Moreover, by applying the SMOTE balancing process, the AUC score of our models increased 13%, while the Recall score increased from 55% to 82%.
Descripción
The registered version of this article, first published in Earth Science Informatics, is available online at the publisher's website: Springer, https://doi.org/10.1007/s12145-025-01710-6
La versión registrada de este artículo, publicado por primera vez en Earth Science Informatics, está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s12145-025-01710-6
This work has been funded by the Ibero-American Programme of Science and Technology for Development, through the project “Agricultural Internet Of Things And Data Analytics To Make Better Decisions In The Precision Agriculture Context (AgIoT)” with ref. CYTED-520RT0011 2019.
La versión registrada de este artículo, publicado por primera vez en Earth Science Informatics, está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s12145-025-01710-6
This work has been funded by the Ibero-American Programme of Science and Technology for Development, through the project “Agricultural Internet Of Things And Data Analytics To Make Better Decisions In The Precision Agriculture Context (AgIoT)” with ref. CYTED-520RT0011 2019.
Categorías UNESCO
Palabras clave
frost forecast, machine learning methods, synthetic minority oversampling technique, frost methodology, frost dataset
Citación
Pérez Tárraga, J., Castillo-Cara, M., Arias-Antúnez, E. et al. Frost forecasting through machine learning algorithms. Earth Sci Inform 18, 183 (2025). https://doi.org/10.1007/s12145-025-01710-6
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial