A Preliminary Study to Solve Crop Frost Prediction Using an Intelligent Data Analysis Process

Precision agriculture has created new opportunities to solve problems in the field of agriculture by balancing investment with higher returns. This paper focuses on the problem of frost in stone fruit crops. These frosts occur due to the large temperature changes caused by climate change, which anticipate the blooming of stone fruit trees due to high temperatures at midday, but damage these flowers with temperatures below zero that occur at night in the last days of winter. The aim of this paper is to perform a preliminary study to predict, with the least possible error, the possible frosts that can occur in crops. Data for this initial study have been obtained from three meteorological stations belonging to the Murcia Institute of Agricultural and Food Research and Development. The purpose of this paper is addressed using two of the techniques offered by intelligent data analysis. Specifically, the M5P regression tree for temperature prediction and the C4.5 decision tree to classify, whether or not there is frost, have been used. Initial results are satisfactory with more than 89% accuracy in classification and an error less than 0.5 degrees Celsius in temperature prediction. In addition, the results identify the most relevant attributes to predict temperature, being some of them dew point, vapor pressure deficit and maximum relative humidity.