Related Experiment Video
Updated: Jun 26, 2026
A High-Resolution, Single-Grain, In Vivo Pollen Hydration Bioassay for Arabidopsis thaliana
Published on: June 30, 2023
Detection of pollen grains in multifocal optical microscopy images of air samples
Sander H Landsmeer1, Emile A Hendriks, Letty A de Weger
1Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands.
Abstract:
Pollen is a major cause of allergy and monitoring pollen in the air is relevant for diagnostic purposes, development of pollen forecasts, and for biomedical and biological researches. Since counting airborne pollen is a time-consuming task and requires specialized personnel, an automated pollen counting system is desirable. In this article, we present a method for detecting pollen in multifocal optical microscopy images of air samples collected by a Burkard pollen sampler, as a first step in an automated pollen counting procedure. Both color and shape information was used to discriminate pollen grains from other airborne material in the images, such as fungal spores and dirt. A training set of 44 images from successive focal planes (stacks) was used to train the system in recognizing pollen color and for optimization. The performance of the system has been evaluated using a separate set of 17 image stacks containing 65 pollen grains, of which 86% was detected. The obtained precision of 61% can still be increased in the next step of classifying the different pollen in such a counting system. These results show that the detection of pollen is feasible in images from a pollen sampler collecting ambient air. This first step in automated pollen detection may form a reliable basis for an automated pollen counting system.
Insights
Automated pollen detection from air samples is now feasible. This method uses image analysis to identify pollen, paving the way for faster allergy monitoring and forecasting.
Area of Science:
- Environmental science
- Biomedical engineering
- Allergy research
Background:
- Airborne pollen monitoring is crucial for allergy diagnosis, forecasting, and research.
- Current manual pollen counting is labor-intensive and requires expertise.
- An automated system is needed to improve efficiency and accuracy.
Purpose of the Study:
- To develop and evaluate a method for detecting pollen in microscopy images.
- To establish the first step towards an automated pollen counting system.
- To differentiate pollen from other airborne particles like fungal spores and dirt.
Main Methods:
- Utilized multifocal optical microscopy images of air samples collected via a Burkard pollen sampler.
- Employed color and shape information for pollen grain detection.
- Trained a system using 44 image stacks and evaluated it on 17 image stacks.
Main Results:
- Successfully detected 86% of pollen grains in a separate test set (65 pollen grains).
- Achieved a precision of 61% in pollen detection.
- Demonstrated the feasibility of automated pollen detection from ambient air samples.
Conclusions:
- Automated pollen detection from air samples is achievable using image analysis.
- This method provides a reliable foundation for developing a fully automated pollen counting system.
- Further refinement is needed for classifying specific pollen types.
