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dc.creatorApolo Apolo, Orly Enriquees
dc.creatorPérez Ruiz, Manueles
dc.creatorMartínez Guanter, Jorgees
dc.creatorValente, Joãoes
dc.date.accessioned2020-10-27T13:54:35Z
dc.date.available2020-10-27T13:54:35Z
dc.date.issued2020
dc.identifier.citationApolo Apolo, O.E., Pérez Ruiz, M., Martínez Guanter, J. y Valente, J. (2020). A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique. Frontiers in Plant Science, 2020 (11) (2020 (1086)), 1 p.-15 p..
dc.identifier.issn1664-462Xes
dc.identifier.urihttps://hdl.handle.net/11441/102278
dc.description.abstractFarmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using offthe-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made in situ by an agrotechnician, and an R2 value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An R2 value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs via optimized orchard management.es
dc.formatapplication/pdfes
dc.format.extent15 p.es
dc.language.isoenges
dc.publisherFrontiers Media S. A.es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep learninges
dc.subjectApplees
dc.subjectYield mapes
dc.subjectGoogle Colabes
dc.subjectPhotogrammetryes
dc.subjectFruites
dc.titleA Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Techniquees
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidoses
dc.relation.publisherversionhttps://doi.org/10.3389/fpls.2020.01086es
dc.identifier.doi10.3389/fpls.2020.01086es
dc.contributor.groupUniversidad de Sevilla. AGR-278: Smart Biosystems Laboratoryes
dc.journaltitleFrontiers in Plant Sciencees
dc.publication.volumen2020 (11)es
dc.publication.issue2020 (1086)es
dc.publication.initialPage1 p.es
dc.publication.endPage15 p.es
dc.identifier.sisius25037es

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