Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

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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.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionCOMPUTER_VISION
dc.contributor.institutionVISUAL
dc.date.issued2019-06
dc.descriptionPublisher Copyright: © 2018 Elsevier B.V.
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.description.statusPeer reviewed
dc.format.extent11
dc.format.extent7538690
dc.identifier.citationPicon , A , Alvarez-Gila , A , Seitz , M , Ortiz-Barredo , A , Echazarra , J & Johannes , A 2019 , ' Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild ' , Computers and Electronics in Agriculture , vol. 161 , pp. 280-290 . https://doi.org/10.1016/j.compag.2018.04.002
dc.identifier.doi10.1016/j.compag.2018.04.002
dc.identifier.issn0168-1699
dc.identifier.otherresearchoutputwizard: 11556/550
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85045718934&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofComputers and Electronics in Agriculture
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsConvolutional neural network
dc.subject.keywordsDeep learning
dc.subject.keywordsImage processing
dc.subject.keywordsPlant disease
dc.subject.keywordsEarly pest
dc.subject.keywordsDisease identification
dc.subject.keywordsPrecision agriculture
dc.subject.keywordsPhytopathology
dc.subject.keywordsConvolutional neural network
dc.subject.keywordsDeep learning
dc.subject.keywordsImage processing
dc.subject.keywordsPlant disease
dc.subject.keywordsEarly pest
dc.subject.keywordsDisease identification
dc.subject.keywordsPrecision agriculture
dc.subject.keywordsPhytopathology
dc.subject.keywordsForestry
dc.subject.keywordsAgronomy and Crop Science
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsHorticulture
dc.subject.keywordsSDG 2 - Zero Hunger
dc.titleDeep convolutional neural networks for mobile capture device-based crop disease classification in the wilden
dc.typejournal article
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