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Pollen Grain Classification Challenge 2020

Challenge Report

  • Conference paper
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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12668))

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Abstract

This report summarises the Pollen Grain Classification Challenge 2020, and the related findings. It serves as an introduction to the technical reports that were submitted to the competition section at the 25th International Conference on Pattern Recognition (ICPR 2020), related to the Pollen Grain Classification Challenge. The challenge is meant to develop automatic pollen grain classification systems, by leveraging on the first large scale annotated dataset of microscope pollen grain images.

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Notes

  1. 1.

    More details about the full dataset can be found at: https://iplab.dmi.unict.it/pollengraindataset/dataset.

  2. 2.

    Check the complete leaderboard ranking at https://iplab.dmi.unict.it/pollenclassificationchallenge/results.

References

  1. Alotaibi, S.S., et al.: Pollen molecular biology: applications in the forensic palynology and future prospects: a review. Saudi J. Biol. Sci. 27(5), 1185–1190 (2020). https://doi.org/10.1016/j.sjbs.2020.02.019

    Article  Google Scholar 

  2. Astolfi, G., et al.: POLLEN73S: an image dataset for pollen grains classification. Ecol. Inf. 60, 101165 (2020). https://doi.org/10.1016/j.ecoinf.2020.101165

    Article  Google Scholar 

  3. Battiato, S., Ortis, A., Trenta, F., Ascari, L., Politi, M., Siniscalco, C.: Detection and classification of pollen grain microscope images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 980–981 (2020)

    Google Scholar 

  4. Battiato, S., Ortis, A., Trenta, F., Ascari, L., Politi, M., Siniscalco, C.: Pollen13k: a large scale microscope pollen grain image dataset. In: IEEE International Conference on Image Processing (ICIP), pp. 2456–2460. IEEE (2020)

    Google Scholar 

  5. Buters, J.T., et al.: Pollen and spore monitoring in the world. Clin. Transl. Allergy 8(1), 1–5 (2018). https://doi.org/10.1186/s13601-018-0197-8

  6. Caillaud, D., Martin, S., Segala, C., Besancenot, J.P., Clot, B., Thibaudon, M.: Effects of airborne birch pollen levels on clinical symptoms of seasonal allergic rhinoconjunctivitis. Int. Arch. Allergy Immunol. 163(1), 43–50 (2014). https://doi.org/10.1159/000355630

    Article  Google Scholar 

  7. Chen, Y., Bai, Y., Zhang, W., Mei, T.: Destruction and construction learning for fine-grained image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5157–5166 (2019)

    Google Scholar 

  8. Crouzy, B., Stella, M., Konzelmann, T., Calpini, B., Clot, B.: All-optical automatic pollen identification: towards an operational system. Atmos. Environ. 140, 202–212 (2016). https://doi.org/10.1016/j.atmosenv.2016.05.062

    Article  Google Scholar 

  9. Cunha, M., Ribeiro, H., Abreu, I.: Pollen-based predictive modelling of wine production: application to an arid region. Eur. J. Agron. 73, 42–54 (2016). https://doi.org/10.1016/j.eja.2015.10.008

    Article  Google Scholar 

  10. Daood, A., Ribeiro, E., Bush, M.: Pollen grain recognition using deep learning. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 321–330. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_30

    Chapter  Google Scholar 

  11. Duller, A., Guller, G., France, I., Lamb, H.: A pollen image database for evaluation of automated identification systems. Quat. Newsl. 89, 4–9 (1999)

    Google Scholar 

  12. Fang, J., Sun, Y., Zhang, Q., Li, Y., Liu, W., Wang, X.: Densely connected search space for more flexible neural architecture search. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10628–10637 (2020)

    Google Scholar 

  13. Fernández-Llamazares, Á., Belmonte, J., Boada, M., Fraixedas, S.: Airborne pollen records and their potential applications to the conservation of biodiversity. Aerobiologia 30(2), 111–122 (2013). https://doi.org/10.1007/s10453-013-9320-4

    Article  Google Scholar 

  14. France, I., Duller, A.W., Duller, G.A., Lamb, H.F.: A new approach to automated pollen analysis. Quat. Sci. Rev. 19(6), 537–546 (2000). https://doi.org/10.1016/S0277-3791(99)00021-9

    Article  Google Scholar 

  15. Gallardo-Caballero, R., García-Orellana, C.J., García-Manso, A., González-Velasco, H.M., Tormo-Molina, R., Macías-Macías, M.: Precise pollen grain detection in bright field microscopy using deep learning techniques. Sensors (Switzerland) 19(16), 1–19 (2019). https://doi.org/10.3390/s19163583

    Article  Google Scholar 

  16. Goncalves, A.B., et al.: Feature extraction and machine learning for the classification of Brazilian savannah pollen grains. PLoS ONE 11(6), e0157044 (2016). https://doi.org/10.1371/journal.pone.0157044

    Article  Google Scholar 

  17. Haddrell, A.E., Thomas, R.J.: Aerobiology: experimental considerations, observations, and future tools. Appl. Environ. Microbiol. 83(17), 1–15 (2017). https://doi.org/10.1128/AEM.00809-17

    Article  Google Scholar 

  18. Hader, J.D., Wright, T.P., Petters, M.D.: Contribution of pollen to atmospheric ice nuclei concentrations. Atmos. Chem. Phys. 14(11), 5433–5449 (2014). https://doi.org/10.5194/acp-14-5433-2014

    Article  Google Scholar 

  19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  20. Holt, K.A., Bennett, K.: Principles and methods for automated palynology. New Phytol. 203(3), 735–742 (2014). https://doi.org/10.1111/nph.12848

    Article  Google Scholar 

  21. Huffman, J.A., et al.: Real-time sensing of bioaerosols: review and current perspectives. Aerosol Sci. Technol. 54(5), 465–495 (2020). https://doi.org/10.1080/02786826.2019.1664724

    Article  Google Scholar 

  22. Jackson, S.L., Bayliss, K.L.: Spore traps need improvement to fulfil plant biosecurity requirements. Plant Pathol. 60(5), 801–810 (2011). https://doi.org/10.1111/j.1365-3059.2011.02445.x

    Article  Google Scholar 

  23. Korobeynikov, A., Kamalova, Y., Palabugin, M., Basov, I.: The use of convolutional neural network LeNet for pollen grains classification. In: “Instrumentation Engineering, Electronics and Telecommunications" Proceedings of the IV International Forum, Izhevsk, Russia, pp. 38–44 (2018). https://doi.org/10.22213/2658-3658-2018-38-44

  24. Li, P., Flenley, J., Empson, L.K.: Classification of 13 types of New Zealand pollen patterns using neural networks. In: IVCNZ (1998)

    Google Scholar 

  25. Mercuri, A.M.: Applied palynology as a trans-disciplinary science: the contribution of aerobiology data to forensic and palaeoenvironmental issues. Aerobiologia 31(3), 323–339 (2015). https://doi.org/10.1007/s10453-015-9367-5

    Article  Google Scholar 

  26. Pearce, D.A., et al.: Aerobiology over Antarctica-a new initiative for atmospheric ecology. Front. Microbiol. 7, 16 (2016). https://doi.org/10.3389/fmicb.2016.00016

    Article  Google Scholar 

  27. Ranzato, M., Taylor, P.E., House, J.M., Flagan, R.C., LeCun, Y., Perona, P.: Automatic recognition of biological particles in microscopic images. Pattern Recogn. Lett. 28(1), 31–39 (2007). https://doi.org/10.1016/j.patrec.2006.06.010

    Article  Google Scholar 

  28. Roy, C.J., Reed, D.S.: Infectious disease aerobiology: miasma incarnate. Front. Cell. Infect. Microbiol. 2(December), 163 (2012). https://doi.org/10.3389/fcimb.2012.00163

    Article  Google Scholar 

  29. Sauvageat, E., et al.: Real-time pollen monitoring using digital holography. Atmos. Meas. Tech. 13(3), 1539–1550 (2020). https://doi.org/10.5194/amt-13-1539-2020

    Article  Google Scholar 

  30. Sevillano, V., Aznarte, J.L.: Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks. PLoS ONE 13(9), 1–18 (2018). https://doi.org/10.1371/journal.pone.0201807

    Article  Google Scholar 

  31. Wang, Y., Morariu, V.I., Davis, L.S.: Learning a discriminative filter bank within a CNN for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4148–4157 (2018)

    Google Scholar 

  32. West, J.S., Kimber, R.: Innovations in air sampling to detect plant pathogens. Ann. Appl. Biol. 166(1), 4–17 (2015). https://doi.org/10.1111/aab.12191

    Article  Google Scholar 

  33. Wu, Y.C., et al.: Air quality monitoring using mobile microscopy and machine learning. Light Sci. Appl. 6(9), e17046 (2017). https://doi.org/10.1038/lsa.2017.46

    Article  Google Scholar 

  34. Yu, C., Zhao, X., Zheng, Q., Zhang, P., You, X.: Hierarchical bilinear pooling for fine-grained visual recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 595–610. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_35

    Chapter  Google Scholar 

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Acknowledgements

The research has been carried out thanks to the collaboration with Ferrero HCo, which financed the project and allowed the collection of aerobiological samples from hazelnut plantations.

Author information

Authors and Affiliations

  1. University of Catania, 95125, Catania, Italy

    Sebastiano Battiato, Francesco Guarnera, Alessandro Ortis & Francesca Trenta

  2. University of Turin, 10125, Turin, Italy

    Lorenzo Ascari & Consolata Siniscalco

  3. Agri Competence Center – Ferrero HCo, Milan, Italy

    Tommaso De Gregorio & Eloy Suárez

Authors
  1. Sebastiano Battiato
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  2. Francesco Guarnera
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  3. Alessandro Ortis
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  4. Francesca Trenta
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  5. Lorenzo Ascari
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  6. Consolata Siniscalco
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  7. Tommaso De Gregorio
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  8. Eloy Suárez
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Corresponding author

Correspondence to Alessandro Ortis .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla,, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Battiato, S. et al. (2021). Pollen Grain Classification Challenge 2020. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_34

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  • DOI: https://doi.org/10.1007/978-3-030-68793-9_34

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