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dc.contributor.authorChaves García, Antonio Jesús
dc.contributor.authorMartín-Fernández, Cristian 
dc.contributor.authorLlopis-Torres, Luis Manuel 
dc.contributor.authorDíaz-Rodríguez, Manuel 
dc.contributor.authorRuiz-Mata, Rocío
dc.contributor.authorGálvez-Montañez, Enrique de
dc.contributor.authorRecio-Criado, María Marta 
dc.contributor.authorTrigo-Pérez, María del Mar 
dc.contributor.authorPicornell Rodríguez, Antonio
dc.date.accessioned2024-01-16T11:09:29Z
dc.date.available2024-01-16T11:09:29Z
dc.date.issued2023-12-20
dc.identifier.citationChaves, A.J., Martín, C., Torres, L.L. et al. Pollen recognition through an open-source web-based system: automated particle counting for aerobiological analysis. Earth Sci Inform (2023). https://doi.org/10.1007/s12145-023-01189-zes_ES
dc.identifier.urihttps://hdl.handle.net/10630/28773
dc.description.abstractAirborne pollen is produced by plants for their sexual reproduction and can have negative impacts on public health. The current monitoring systems are based on manual sampling processes which are tedious and time-consuming. Due to that, pollen concentrations are often reported with a delay of up to one week. In this study, we present an open-source user-friendly web application powered by deep learning for automatic pollen count and classification. The application aims to simplify the process for non-IT users to count and classify different types of pollen, reducing the effort required compared to manual methods. To overcome the challenges of acquiring large labelled datasets, we propose a semi-automatic labelling approach, which combines human expertise and machine learning techniques. The results demonstrate that our approach significantly reduces the effort required for users to count and classify pollen taxa accurately. The model achieved high precision and recall rates (> 96% mAP@0.5), enabling reliable pollen identification and prediction.es_ES
dc.description.sponsorshipFunding for open access charge: Universidad de Málaga/CBUA. This work was financed by the Ministry of Science and Innovation of Spain and FEDER funding inside the Operational Plurir- regional Program of Spain 2014–2020 and the Operational Program of Smart Growing (Environmental and Biodiversity Climate Change Lab, EnBiC2-Lab; LIFEWATCH-2019-11-UMA-01-BD) and by the Span- ish project TED2021-130167B-C33 (‘GEDIER: Application of Digital Twins to more sustainable irrigated farms’). A. Picornell was supported by a postdoctoral grant financed by the Ministry of Economic Transfor- mation, Industry, Knowledge and Universities of the Junta de Andalucía (POSTDOC_21_00056).es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectInformática - Aplicacioneses_ES
dc.subjectPolen - Dispersiónes_ES
dc.subject.otherPollen measurementes_ES
dc.subject.otherObject detectiones_ES
dc.subject.otherDeep learninges_ES
dc.subject.otherOpen sourcees_ES
dc.subject.otherApplicationes_ES
dc.titlePollen recognition through an open-source web-based system: automated particle counting for aerobiological analysises_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doi10.1007/s12145-023-01189-z
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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