Abstract
Coffee production is the main agricultural activity in Colombia. More than 350.000 Colombian families depend on coffee harvest. Since coffee rust disease was first reported in the country in 1983, these families have had to face severe consequences. Recently, machine learning approaches have built a dataset for monitoring coffee rust incidence that involves weather conditions and physic crop properties. This background encouraged us to build a dataset for coffee rust detection in Colombian crops through data mining process as Cross Industry Standard Process for Data Mining (CRISP-DM). In this paper we define a proper data to generate accurate models; once the dataset is built, this is tested using classifiers as: Support Vector Regression, Backpropagation Neural Networks and Regression Trees.
Highlights
Coffee production is the main agricultural activity in Colombia
This section describes the data collection process and the generation of datasets used in experiments, and introduces three classifiers for prediction: Support Vector Regression, Backpropagation Neural Network, and Regression Tree M5
A) Performance Evaluation Methods Pearson correlation coefficient (PCC) In statistics, Pearson correlation coefficient is a measure of how well a linear equation describes the relation between two variables and measured on the same object or organism
Summary
Coffee production is the main agricultural activity in Colombia. More than 50 percent of the country’s coffee crop is still susceptible in the productive phase. Studies on coffee rust have concluded that the spores carrying the infection are spread by climatic elements such as wind and rainfall (Becker, 1979). Once spores make contact with a susceptible leaf, the infection process is increased by high shadow index, high humidity (atmosphere and leaf), soil acidity, high coffee tree density and low soil fertility. The dataset proposed joins each of the favorable conditions that coffee rust requires to infect the crop, by taking prophylactic measures (biological, chemical and weather), in order to allow the prevention of the onset of the disease
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