Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation

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...

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Bibliographic Details
Published in:Applied artificial intelligence 2023-12, Vol.37 (1)
Main Authors: Matavulj, Predrag, Panić, Marko, Šikoparija, Branko, Tešendić, Danijela, Radovanović, Miloš, Brdar, Sanja
Format: Article
Language:English
Subjects:
Artificial neural networks
Bioaerosols
Classification
Pollen
Real time
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Summary: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%.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2022.2157593