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dc.contributor.authorFazari, Antonio
dc.contributor.authorPellicer-Valero, Óscar
dc.contributor.authorGómez-Sanchís, Juan
dc.contributor.authorBernardi, Bruno
dc.contributor.authorCubero, Sergio
dc.contributor.authorBenalia, Souraya
dc.contributor.authorZimbalatti, Giuseppe
dc.contributor.authorBlasco, José
dc.date.accessioned2022-03-15T12:41:18Z
dc.date.available2022-03-15T12:41:18Z
dc.date.issued2021es
dc.identifier.citationFazari, A., Pellicer-Valero, O. J., Gómez-Sanchıs, J., Bernardi, B., Cubero, S., Benalia, S. et al.(2021). Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images. Computers and Electronics in Agriculture, 187, 106252.es
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/20.500.11939/7962
dc.description.abstractAnthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 architecture was chosen and adapted to process 61-band hyperspectral images with only two classes. The result showed that the applied model is very effective in detecting infected olives since the sensitivity of the method was very high from the beginning (85% on day 3 and 100% onwards). From a commercial point of view, these results align with the need to detect the maximum number of infected fruits.es
dc.language.isoenes
dc.publisherElsevieres
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectQuality inspectiones
dc.subjectSpectral imaginges
dc.titleApplication of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral imageses
dc.typecontributionToPeriodicales
dc.authorAddressblasco_josiva@gva.eses
dc.identifier.doi10.1016/j.compag.2021.106252es
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0168169921002696es
dc.journal.issueNumber187es
dc.journal.titleComputers and Electronics in Agriculturees
dc.page.final106252es
dc.page.initial106252es
dc.relation.projectIDThis work is co-funded by the projects AEI PID2019-107347RRC31, PID2019-107347RR-C33, IVIA-GVA 51918 and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014–2020.es
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrisN01 Agricultural engineeringes
dc.subject.agrisU30 Research methodses
dc.subject.agrisH20 Plant diseaseses
dc.subject.agrovocOlea europaeaes
dc.subject.agrovocqualityes
dc.subject.agrovocFungies
dc.subject.agrovocComputer visiones


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