A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia

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dc.citation.journalTitleApplied Sciences-Basel
dc.contributor.authorVelasquez, D.
dc.contributor.authorSánchez, A.
dc.contributor.authorSarmiento Garavito, Sebastián
dc.contributor.authorToro, M.
dc.contributor.authorMaiza Galparsoro, Mikel
dc.contributor.authorSierra Araujo, Basilio
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.contributor.researchgroupEstudios en Mantenimiento (GEMI)spa
dc.date.accessioned2020-01-15
dc.date.accessioned2021-04-12T19:12:49Z
dc.date.available2021-04-12T19:12:49Z
dc.date.issued2020-01-19
dc.date.submitted2019-12-20
dc.description.abstractAgricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers. One of these pests is Coffee Leaf Rust (CLR), which is a fungal epidemic disease that affects coffee trees and causes massive defoliation. As an example, this disease has been affecting coffee trees in Colombia (the third largest producer of coffee worldwide) since the 1980s, leading to devastating losses between 70% and 80% of the harvest. Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products. The most common way to detect this disease is by walking through the crop and performing a human visual inspection. As a result of this problem, different research studies have proven that technological methods can help to identify these pathogens. Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica, Caturra variety,scale crop through the technological integration of remote sensing (through drone capable multispectral cameras), wireless sensor networks (multisensor approach), and Deep Learning (DL) techniques. Our diagnostic model achieved an F1-score of 0.775. The analysis of the results revealed a p-value of 0.231, which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant. The above shows that both methods were significantly similar to diagnose the disease. © 2020 by the authors.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9982
dc.identifier.doi10.3390/app10020697
dc.identifier.issn20763417
dc.identifier.issn14545101
dc.identifier.otherWOS;000522540400273
dc.identifier.otherSCOPUS;2-s2.0-85081237683
dc.identifier.urihttp://hdl.handle.net/10784/28349
dc.language.isoeng
dc.publisherUniversitatea Politehnica Bucuresti
dc.relationDOI;10.3390/app10020697
dc.relationWOS;000522540400273
dc.relationSCOPUS;2-s2.0-85081237683
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081237683&doi=10.3390%2fapp10020697&partnerID=40&md5=8eb757fae70cab7f3d7ef8bdf0f6f2fd
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/2076-3417
dc.sourceApplied Sciences-Basel
dc.subjectAgriculture 4.0eng
dc.subjectCoffee leaf rusteng
dc.subjectDeep learningeng
dc.subjectFourth Industrial Revolutioneng
dc.subjectMachine learningeng
dc.subjectRemote sensingeng
dc.titleA Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombiaeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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