RT Journal Article
T1 Pollen recognition through an open-source web-based system: automated particle counting for aerobiological analysis
A1 Chaves García, Antonio Jesús
A1 Martín-Fernández, Cristian
A1 Llopis-Torres, Luis Manuel
A1 Díaz-Rodríguez, Manuel
A1 Ruiz-Mata, Rocío
A1 Gálvez-Montañez, Enrique de
A1 Recio-Criado, María Marta
A1 Trigo-Pérez, María del Mar
A1 Picornell Rodríguez, Antonio
K1 Informática - Aplicaciones
K1 Polen - Dispersión
AB Airborne 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.
PB Springer Nature
YR 2023
FD 2023-12-20
LK https://hdl.handle.net/10630/28773
UL https://hdl.handle.net/10630/28773
LA eng
NO Chaves, 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-z
NO Funding 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).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026