Mostrar el registro sencillo del ítem

Artículo

dc.creatorDomínguez Cid, Samueles
dc.creatorLarios Marín, Diego Franciscoes
dc.creatorBarbancho Concejero, Julioes
dc.creatorMolina Cantero, Francisco Javieres
dc.creatorGuerra Coronado, Javier Antonioes
dc.creatorLeón de Mora, Carloses
dc.date.accessioned2024-04-01T10:33:00Z
dc.date.available2024-04-01T10:33:00Z
dc.date.issued2024
dc.identifier.citationDomínguez Cid, S., Larios Marín, D.F., Barbancho Concejero, J., Molina Cantero, F.J., Guerra Coronado, J.A. y León de Mora, C. (2024). Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models. Sensors, 24 (5), Article number 1370. https://doi.org/10.3390/s24051370.
dc.identifier.issn1424 - 8220es
dc.identifier.urihttps://hdl.handle.net/11441/156565
dc.description.abstractDuring the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications.es
dc.description.sponsorshipUniversidad de Sevillaes
dc.formatapplication/pdfes
dc.format.extent17 p.es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofSensors, 24 (5), Article number 1370.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHyperspectral imaginges
dc.subjectOliveses
dc.subjectPrecision agriculturees
dc.subjectMachine learninges
dc.subjectPattern recognitiones
dc.titleIdentification of Olives Using In-Field Hyperspectral Imaging with Lightweight Modelses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Tecnología Electrónicaes
dc.relation.projectIDPYC20 RE 090 USes
dc.relation.projectID802C2000097es
dc.relation.projectID2021/C005/0014786es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/24/5/1370es
dc.identifier.doi10.3390/s24051370es
dc.journaltitleSensorses
dc.publication.volumen24es
dc.publication.issue5es
dc.publication.initialPageArticle number 1370es
dc.contributor.funderUniversidad de Sevillaes

FicherosTamañoFormatoVerDescripción
Idenfifications of olives.pdf4.391MbIcon   [PDF] Ver/Abrir  

Este registro aparece en las siguientes colecciones

Mostrar el registro sencillo del ítem

Atribución 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como: Atribución 4.0 Internacional