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dc.contributor.authorJohannes, Alexander
dc.contributor.authorPicon, Artzai
dc.contributor.authorAlvarez-Gila, Aitor
dc.contributor.authorEchazarra, Jone
dc.contributor.authorRodriguez-Vaamonde, Sergio
dc.contributor.authorNavajas, Ana Díez
dc.contributor.authorOrtiz-Barredo, Amaia
dc.date.accessioned2017-05-09T09:11:53Z
dc.date.available2017-05-09T09:11:53Z
dc.date.issued2017-06-1
dc.identifier.citationJohannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas AD, et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture [Internet]. Elsevier BV; 2017 Jun;138:200–9. Available from: http://dx.doi.org/10.1016/j.compag.2017.04.013
dc.identifier.issn0168-1699en
dc.identifier.urihttp://hdl.handle.net/11556/397
dc.description.abstractDisease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic –ROC– Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions.en
dc.language.isoengen
dc.publisherELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAutomatic plant disease diagnosis using mobile capture devices, applied on a wheat use caseen
dc.typearticleen
dc.identifier.doi10.1016/j.compag.2017.04.013en
dc.isiYesen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsPlant diseaseen
dc.subject.keywordsDiagnosis
dc.subject.keywordsMobile capture devices
dc.identifier.essn1872-7107en
dc.journal.titleComputers and Electronics in Agricultureen
dc.page.final209en
dc.page.initial200en
dc.volume.number138en


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