Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

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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.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionCOMPUTER_VISION
dc.contributor.institutionVISUAL
dc.date.issued2017-06-01
dc.descriptionPublisher Copyright: © 2017 Elsevier B.V.
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.description.statusPeer reviewed
dc.format.extent10
dc.format.extent2888752
dc.identifier.citationJohannes , A , Picon , A , Alvarez-Gila , A , Echazarra , J , Rodriguez-Vaamonde , S , Navajas , A D & Ortiz-Barredo , A 2017 , ' Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case ' , Computers and Electronics in Agriculture , vol. 138 , pp. 200-209 . https://doi.org/10.1016/j.compag.2017.04.013
dc.identifier.doi10.1016/j.compag.2017.04.013
dc.identifier.issn1872-7107
dc.identifier.otherresearchoutputwizard: 11556/397
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85018415591&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofComputers and Electronics in Agriculture
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsPlant disease
dc.subject.keywordsDiagnosis
dc.subject.keywordsMobile capture devices
dc.subject.keywordsPlant disease
dc.subject.keywordsDiagnosis
dc.subject.keywordsMobile capture devices
dc.subject.keywordsForestry
dc.subject.keywordsAgronomy and Crop Science
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsHorticulture
dc.subject.keywordsSDG 2 - Zero Hunger
dc.titleAutomatic plant disease diagnosis using mobile capture devices, applied on a wheat use caseen
dc.typejournal article
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