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dc.contributor.authorPicon, Artzai
dc.contributor.authorAlvarez-Gila, Aitor
dc.contributor.authorSeitz, Maximiliam
dc.contributor.authorOrtiz-Barredo, Amaia
dc.contributor.authorEchazarra, Jone
dc.contributor.authorJohannes, Alexander
dc.date.accessioned2018-05-15T09:18:06Z
dc.date.available2018-05-15T09:18:06Z
dc.date.issued2019-06
dc.identifier.citationPicon, Artzai, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, and Alexander Johannes. “Deep Convolutional Neural Networks for Mobile Capture Device-Based Crop Disease Classification in the Wild.” Computers and Electronics in Agriculture 161 (June 2019): 280–290. doi:10.1016/j.compag.2018.04.002.
dc.identifier.issn0168-1699en
dc.identifier.urihttp://hdl.handle.net/11556/550
dc.description.abstractFungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita).en
dc.language.isoengen
dc.publisherElsevier B.V.en
dc.titleDeep convolutional neural networks for mobile capture device-based crop disease classification in the wilden
dc.typearticleen
dc.identifier.doi10.1016/j.compag.2018.04.002en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsConvolutional neural networken
dc.subject.keywordsDeep learningen
dc.subject.keywordsImage processingen
dc.subject.keywordsPlant diseaseen
dc.subject.keywordsEarly pesten
dc.subject.keywordsDisease identificationen
dc.subject.keywordsPrecision agricultureen
dc.subject.keywordsPhytopathologyen
dc.journal.titleComputers and Electronics in Agricultureen
dc.page.final290
dc.page.initial280
dc.volume.number161


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