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Towards Automation of Pollen Monitoring: Image-Based Tree Pollen Recognition

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Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

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Abstract

Pollen monitoring helps predict the risk of pollen-induced allergies. Traditionally, this monitoring is performed based on the biological material obtained from volumetric Hirst’s traps. A palynological specialist analyzes the obtained microbiological specimen under the microscope, and recognizes and counts pollen grains of various taxa. This is a tedious task, and automatic detection and counting of pollen grains in digital microscopic images can support specialists in their work. YOLOv5 and Faster R-CNN are the state-of-art deep neural networks used for object detection in many fields of computer vision. In the presented research, these detectors were applied to analyze specimen with pollen grains of four taxa, typical of early spring in Central and Eastern Europe. The obtained results enabled the selection of the detector that should be the first choice in pollen grains recognition tasks. Statistical analysis of differences in the distribution of the recognition quality measures also supports the conclusions.

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Authors and Affiliations

  1. Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Lublin, Poland

    Elżbieta Kubera, Agnieszka Kubik-Komar & Paweł Kurasiński

  2. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Alicja Wieczorkowska

  3. Department of Botany and Plant Physiology, University of Life Sciences in Lublin, Lublin, Poland

    Krystyna Piotrowska-Weryszko & Agata Konarska

Authors
  1. Elżbieta Kubera
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  2. Agnieszka Kubik-Komar
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  3. Alicja Wieczorkowska
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  4. Krystyna Piotrowska-Weryszko
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  5. Paweł Kurasiński
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  6. Agata Konarska
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Corresponding authors

Correspondence to Elżbieta Kubera , Agnieszka Kubik-Komar , Alicja Wieczorkowska , Krystyna Piotrowska-Weryszko , Paweł Kurasiński or Agata Konarska .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università della Calabria, Rende, Italy

    Sergio Flesca

  3. Università Federico II di Napoli, Naples, Italy

    Elio Masciari

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Kubera, E., Kubik-Komar, A., Wieczorkowska, A., Piotrowska-Weryszko, K., Kurasiński, P., Konarska, A. (2022). Towards Automation of Pollen Monitoring: Image-Based Tree Pollen Recognition. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_21

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  • DOI: https://doi.org/10.1007/978-3-031-16564-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16563-4

  • Online ISBN: 978-3-031-16564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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