Please use this identifier to cite or link to this item: http://repositorio.unj.edu.pe/handle/UNJ/741
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dc.contributor.authorOcaña Zúñiga,Candy Lisbethes_ES
dc.contributor.authorQuiñones Huatangari,Lenines_ES
dc.contributor.authorHuaccha Castillo,Annick Estefanyes_ES
dc.contributor.authorMilla Pino,Manuel Emilioes_ES
dc.date.accessioned2024-10-09T04:31:24Z-
dc.date.available2024-10-09T04:31:24Z-
dc.date.issued2022-09-03-
dc.identifier.urihttp://repositorio.unj.edu.pe/handle/UNJ/741-
dc.description.abstractHemileia 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 plantationes_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherOnLine Journal of Biological Scienceses_ES
dc.relation.ispartofOnLine Journal of Biological Scienceses_ES
dc.relation.ispartofOnLine Journal of Biological Scienceses_ES
dc.relation.urihttps://doi.org/10.3844/ojbsci.2022.157.164es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceUniversidad Nacional de Jaén||Repositorio Institucional - UNJes_ES
dc.subjectPlant Product, Simulation Model, Statistical Inference, Statistical Inference, Hemileia Vastatrixes_ES
dc.titleDetection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic Reviewes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doihttps://doi.org/10.3844/ojbsci.2022.157.164es_ES
dc.publisher.countryUSes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#4.01.02es_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
renati.author.dni44798819es_ES
renati.author.dni42821048es_ES
renati.author.dni72552959es_ES
renati.author.dni60387747es_ES
Appears in Collections:Artículos Científicos UNJ

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