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Towards Automation of Pollen Monitoring - Dealing with the Background in Pollen Monitoring Images

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

Abstract

Many people suffer from pollen allergies. Therefore, pollen monitoring is performed worldwide, and pollen traps are used for this purpose. Specialists are analyzing the acquired material, using adequate reference material for the identification of target taxa. However, the background in reference images is relatively clean and uniform, whereas the background in pollen trap images contains air bubbles, dust, fungal and fern spores, and pollen grains of various taxa. In this work, we address the automatic detection and identification of the pollen grains of selected tree species, responsible for most common allergies, in images from pollen traps. Deep neural networks have been applied for this purpose, using models trained on reference pollen grain images. These models produce unsatisfactory results when applied to images from pollen traps. Problems related to the background in these images are discussed in this paper, and fine-tuning of the models for the recognition of allergenic pollen grains is presented. The obtained results are discussed, and future works are indicated.

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References

  1. Papers with Code - COCO test-dev Benchmark (Object Detection). https://paperswithcode.com/sota/object-detection-on-coco

  2. AAFA: 2023 Allergy Capitals (2023). https://aafa.org/wp-content/uploads/2023/03/aafa-2023-allergy-capitals-report.pdf

  3. Azevedo, P.: Object detection state of the art 2022 (2022). https://medium.com/@pedroazevedo6/object-detection-state-of-the-art-2022-ad750e0f6003

  4. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. CoRR abs/2005.12872 (2020). https://arxiv.org/abs/2005.12872, www.github.com/facebookresearch/detr

  5. Clot, B., et al.: The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe. Aerobiologia (2020). https://doi.org/10.1007/s10453-020-09666-4

    Article  MATH  Google Scholar 

  6. Fang, Y., et al.: EVA: exploring the limits of masked visual representation learning at scale (2022). http://arxiv.org/abs/2211.07636 [cs] version: 2

  7. Jiang, C., et al.: Field evaluation of an automated pollen sensor. Int. J. Environ. Res. Public Health 19(11), 6444 (2022). https://doi.org/10.3390/ijerph19116444

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  8. Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B.: A review of Yolo algorithm developments. Procedia Comput. Sci. 199, 1066–1073 (2022). https://doi.org/10.1016/j.procs.2022.01.135, 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021): Developing Global Digital Economy after COVID-19

  9. Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLOv8, version 8.0.0, license AGPL-3.0 (2023). https://github.com/ultralytics/ultralytics/blob/main/docs/en/models/yolov8.md

  10. Kejriwal, K.: YOLOv7: The most advanced object detection algorithm? (2023). https://www.unite.ai/yolov7/, unite.AI

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  MATH  Google Scholar 

  12. Kubera, E., Kubik-Komar, A., Kurasiński, P., Piotrowska-Weryszko, K., Skrzypiec, M.: Detection and recognition of pollen grains in multilabel microscopic images. Sensors 22(7), 2690 (2022). https://doi.org/10.3390/s22072690

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  13. Kubera, E., Kubik-Komar, A., Wieczorkowska, A., Piotrowska-Weryszko, K., Kurasiński, P., Konarska, A.: 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. LNCS, vol. 13515, pp. 219–229. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16564-1_21

  14. Lin, T.Y., et al.: COCO - Common Objects in Context (2014). https://arxiv.org/abs/1405.0312

  15. Pinheiro, J., Bates, D., R Core Team: nlme: linear and nonlinear mixed effects models (2023). https://CRAN.R-project.org/package=nlme, r package version 3.1-162

  16. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022). https://www.R-project.org/

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Proceedings of the Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc., Montreal, Canada (2015)

    Google Scholar 

  18. Wang, W., et al.: InternImage: exploring large-scale vision foundation models with deformable convolutions (2023). http://arxiv.org/abs/2211.05778 [cs] version: 4

  19. Zong, Z., Song, G., Liu, Y.: DETRs with collaborative hybrid assignments training (2023). http://arxiv.org/abs/2211.12860 [cs] version: 4

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Correspondence to Alicja Wieczorkowska .

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Kubera, E., Wieczorkowska, A., Piotrowska-Weryszko, K., Konarska, A., Kubik-Komar, A. (2025). Towards Automation of Pollen Monitoring - Dealing with the Background in Pollen Monitoring Images. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2135. Springer, Cham. https://doi.org/10.1007/978-3-031-74633-8_44

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  • DOI: https://doi.org/10.1007/978-3-031-74633-8_44

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