Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation

ABSTRACT

Allergenic pollen affects the quality of life for over 30% of the European population. Since the treatment efficacy is highly related to the actual exposure to pollen, information about the type and number of airborne pollen grains in real-time is essential for reducing their impact. Therefore, the automation of pollen monitoring has become an important research topic. Our study is focused on the Rapid-E real-time bioaerosol detector. So far, vanilla convolutional neural networks (CNNs) are the only deep architectures evaluated for pollen classification on multi-modal Rapid-E data obtained by exposing collected pollen samples of known classes to the device in a controlled environment. This study contributes to the further development of pollen classification models on Rapid-E data by experimenting with more advanced concepts of CNNs, residual, and inception networks. Our experiments included a comprehensive comparison of different CNN architectures, and obtained results provided valuable insights into which convolutional blocks improve pollen classification. We propose a new model which, coupled with a specific training strategy, improves the current state-of-the-art by reducing its relative error rate by 9%.

Acknowledgments

We acknowledge support from COST Action CA18226 “New approaches in detection of pathogens and aeroallergens (ADOPT)” (www.cost.eu/actions/CA18226). This research was funded by the BREATHE project from the Science Fund of the Republic of Serbia PROMIS program, grant agreement no. 6039613, and by the Ministry of Education, Science and Technological Development of the Republic of Serbia, grant agreement no. 451-03-68/2022-14/200358. This research was supported by the RealForAll project (2017HR-RS151), co-financed by the Interreg IPA Cross-border Cooperation program Croatia – Serbia 2014-2020 and Provincial secretariat for finances, Autonomous Province Vojvodina, Republic of Serbia, contract no. 102-401-337/2017-02-4-35-8.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia [451-03-68/2022-14/200358]; Science Fund of the Republic of Serbia [6039613].