Title: Estimation of coffee rust infection and growth through two-level classifier ensembles based on expert knowledge

Authors: David Camilo Corrales; Emmanuel Lasso; Apolinar Figueroa Casas; Agapito Ledezma; Juan Carlos Corrales

Addresses: Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán, Colombia; Departamento de Ciencias de la Computación e Ingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 – Leganés, Spain ' Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán, Colombia ' Grupo de Estudios Ambientales, Universidad del Cauca, Carrera 2 No. 1A-25 – Urbanización Caldas, Popayán, Colombia ' Departamento de Ciencias de la Computación e Ingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 – Leganés, Spain ' Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán, Colombia

Abstract: Rust is a disease that leads to considerable losses in the worldwide coffee industry. There are many contributing factors to the onset of coffee rust, e.g., crop management decisions and the prevailing weather. In Colombia the coffee production has been considerably reduced by 31% on average during the epidemic years compared with 2007. Recent research efforts focus on detection of disease incidence using computer science techniques such as supervised learning algorithms. However, a number of different authors demonstrate that results are not sufficiently accurate using a single classifier. Authors in the computer field propose alternatives for this problem, making use of techniques that combine classifier results. Nevertheless, the traditional approaches have a limited performance due to dataset absence. Therefore, we proposed two-level classifier ensembles for coffee rust infection and growth estimation in Colombian crops, based on expert knowledge.

Keywords: coffee; rust; classifier; ensemble; dataset; expert; knowledge.

DOI: 10.1504/IJBIDM.2018.094984

International Journal of Business Intelligence and Data Mining, 2018 Vol.13 No.4, pp.369 - 387

Available online: 17 Jan 2018 *

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