Pollen recognition through an open-source web-based system: automated particle counting for aerobiological analysis

• Published in: Earth science informatics 2024-02, Vol.17 (1), p.699-710
• Main Authors: Chaves, Antonio Jesús, Martín, Cristian, Torres, Luis Llopis, Díaz, Manuel, Ruiz-Mata, Rocío, de Gálvez-Montañez, Enrique, Recio, Marta, Trigo, M. Mar, Picornell, Antonio
• Format: Article
• Language: English
• Subjects: Algorithms; Applications programs; Automation; Classification; Datasets; Deep learning; Earth and Environmental Science; Earth science; Earth Sciences; Earth System Sciences; Flowers & plants; Informatics; Information Systems Applications (incl.Internet); Labeling; Machine learning; Ontology; Open source software; Pollen; Pollen concentrations; Pollen count; Public health; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-023-01189-z

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.