Research Article Open Access

Detection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic Review

Lenin Quiñones Huatangari1, Candy Lisbeth Ocaña Zúñiga1, Annick Estefany Huaccha Castillo1, Rubén Eusebio Acosta Jacinto2, Manuel Emilio Milla Pino1, Milton Ríos Julcapoma2, Ricardo Yauri Rodríguez2, Eduardo Mendoza Villaizán2 and Aladino Pérez Cabrera3
  • 1 Institute of Data Science, National University of Jaen, Peru
  • 2 National Institute for Telecommunications Research and Training, National University of Engineering, Peru
  • 3 Department of Agriculture, Cooperativa Agraria Cafetalera La Prosperidad de Chirinos, Peru

Abstract

Hemileia vastatrix is a fungus that causes coffee rust disease and, depending on the level of severity, reduces the photosynthetic capacity of the plant and of new shoots, leading to low coffee yields and even death; its symptoms are visible on the leaf. Systems based on computer algorithms have been developed to predict diseases and pests in coffee. The objective of the manuscript was to analyse the detection of rust occurrence in coffee plantations, through field determinations of climatological, agronomic and crop management variables using data mining algorithms. A systematic review of studies published from 2001 to 2021 was carried out in the Scopus, Ebsco Host and Scielo databases, considering as an inclusion criterion the works that used experimental design in data collection. The studies included in this review were 22, 64% of which came from the top two coffee-roducing countries in Latin America (Brazil and Colombia); the analysis of these studies revealed that the input variables were climatic, soil fertility properties, management and physical properties of the crops. In addition, they used supervised (decision tree, artificial neural networks, multiple linear regression, among others) and unsupervised (clustering) algorithms, with the support of experts in the study of the fungus and used statistics such as coefficient of determination, root mean square error, among others, to validate the proposals. Overall, this systematic review provides evidence of the effectiveness of data mining algorithms implemented to detect the occurrence of rust in coffee plantations.

OnLine Journal of Biological Sciences
Volume 22 No. 2, 2022, 157-164

DOI: https://doi.org/10.3844/ojbsci.2022.157.164

Submitted On: 6 October 2021 Published On: 31 March 2022

How to Cite: Huatangari, L. Q., Ocaña Zúñiga, C. L., Huaccha Castillo, A. E., Acosta Jacinto, R. E., Milla Pino, M. E., Julcapoma, M. R., Rodríguez, R. Y., Villaizán, E. M. & Cabrera, A. P. (2022). Detection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic Review. OnLine Journal of Biological Sciences, 22(2), 157-164. https://doi.org/10.3844/ojbsci.2022.157.164

  • 747 Views
  • 266 Downloads
  • 0 Citations

Download

Keywords

  • Plant Product
  • Simulation Model
  • Statistical Inference
  • Statistical Inference
  • Hemileia Vastatrix