Machine Learning for Prediction of Frost Episodes in the Maule Region of Chile

Frosts are one of the main risks faced by farmers during the winter and spring seasons. These events can cause significant damage to diverse types of crops. In Chile, these frost generates significant losses in the agricultural production sector, causing crop losses of an entire year and compromising the income of the following year, especially fruit and wine makers. In this work we developed a prediction model based on historical agrometeorological information able to predict efficiently and up to 12 h earlier the occurrence of a frost event in the Maule Region of Chile. Various algorithms and machine learning methods were evaluated, we found that Random Forest exhibits the best results overall. The results obtained in the frost prediction reach over (90%) of efficiency in most of the evaluated scenarios.