Affiliations: [a] Department of Telematics Engineering, Engineering Telematics Group, University of Cauca, Popayán, Colombia
| [b] CIRAD, UPR Bioagresseurs analyse et maîtrise du risque, Montpellier, France, Department of Research and Development, CATIE, Turrialba, Costa Rica
| [c] Computer Science and Engineering Department, CAOS Research Group, Carlos III University of Madrid (UC3M)
Correspondence:
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Corresponding author. Edwar Javier Girón Buitrón, Department of Telematics Engineering, Engineering Telematics Group, University of Cauca, Popayán, Colombia. E-mail: edwardgb@unicauca.edu.co.
Abstract: The coffee rust is a devastating disease that causes large economic losses across the world. The severity of this disease changes over time so the farmers are not fully aware of the economic importance of the rust disease in the coffee crops. From a computational science perspective, several investigations have been proposed to decrease the effects caused by the coffee rust appearance from Expert systems based on machine learning techniques. However, because samples about coffee rust incidence are few, the rules created from machine learning techniques do not contain enough information to consider the diversity of scenarios for detecting coffee rust. This paper proposes an expert system based on rules, where the rules are created considering the expert knowledge of specialists and technical reports about the behavior of the disease during a crop year. As far as we know, this is the first expert system proposed using not only expert knowledge but also technical reports in the coffee rust problem. The Buchanan methodology is used to design the proposed system. Experiment results present an average accuracy of 66,67% to detect a correct warning of coffee rust levels.
Keywords: Decision support system, crops, disease, agriculture, hemileia vastatrix
