Early prediction of frost events in high altitude crops, using machine learning methods

Abstract

In the tropic, many crops are distributed in the highlands of provinces of the Andean regions at heights of 2, 500 m asl and constitute the areas with the highest susceptibility to the frost events occurrence. The study objective was to propose an early frost prediction model based on the relationships between frost events and climatic variables, modeled with machine learning methods. The climatic variables were obtained from thirteen meteorological stations located inside flower crops and distributed in nine municipalities of the Cundinamarca Department. The variables registered were temperature, relative humidity, dew point, photosynthetically active radiation, and precipitation, entered as explanatory variables of frost events. The metrics used for predictive performance evaluation of the five machine learning methods examined were precision, recall, true negative rate, accuracy, and F1 score. The variables’ climatic behavior of previous hours to a frost event are low humidity, wind speed and cloudiness, and high thermal radiation. The fourth of the five trained models performed well due to their classification evaluation metrics, greater than 91%. The cross-validation and statistical analysis demonstrated the higher accuracy of the GBDT model on frost events detection.