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Pollen counts can be predicted by machine learning models using meteorological data with more than 80% accuracy even a week ahead, for both grass and birch tree pollen, which could be key in effectively treating hayfever

02.18.26 | PLOS

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Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season
Graphical abstract. Credit: Bulanda et al., 2026, PLOS One, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Pollen counts can be predicted by machine learning models using meteorological data with more than 80% accuracy even a week ahead, for both grass and birch tree pollen, which could be key in effectively treating hayfever

Article URL : https://plos.io/3NW0x7X

Article title: Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season

Author countries: Poland

Funding: The study was supported by the statutory project of the Ministry of Science and Higher Education in Poland N41/DBS/001323. Initials of the authors who received the award: MB. URL of the funder: https://www.gov.pl/web/science . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PLOS One

10.1371/journal.pone.0332093

Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season

18-Feb-2026

The authors have declared that no competing interests exist.

Keywords

Artificial Intelligence Computer Science Life Sciences Plant Physiology Plant Reproduction Pollen

Article Information

Journal
PLOS One
DOI
10.1371/journal.pone.0332093
Article Publication Date
2026-02-18
Article Title
Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season
COI Statement
The authors have declared that no competing interests exist.

Contact Information

Hanna Abdallah
PLOS
onepress@plos.org

How to Cite This Article

APA:
PLOS. (2026, February 18). Pollen counts can be predicted by machine learning models using meteorological data with more than 80% accuracy even a week ahead, for both grass and birch tree pollen, which could be key in effectively treating hayfever. Brightsurf News. https://www.brightsurf.com/news/LVDEJO5L/pollen-counts-can-be-predicted-by-machine-learning-models-using-meteorological-data-with-more-than-80-accuracy-even-a-week-ahead-for-both-grass-and-birch-tree-pollen-which-could-be-key-in-effectively.html
MLA:
"Pollen counts can be predicted by machine learning models using meteorological data with more than 80% accuracy even a week ahead, for both grass and birch tree pollen, which could be key in effectively treating hayfever." Brightsurf News, Feb. 18 2026, https://www.brightsurf.com/news/LVDEJO5L/pollen-counts-can-be-predicted-by-machine-learning-models-using-meteorological-data-with-more-than-80-accuracy-even-a-week-ahead-for-both-grass-and-birch-tree-pollen-which-could-be-key-in-effectively.html.