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A Two-Stage, Semi-Supervised Deep Learning Framework for the Detection and Classification of Ambient Pollen using

Sachin Dhawan¹, Anuj Saxena², Anand Kumar²

¹School of Interdisciplinary Research, Indian Institute of Technology Delhi, Delhi 110016, India.

Environmental Science & Technology

|June 23, 2026

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Evanescent wave scattering microscopy (EWSM) offers a novel, label-free method for identifying airborne pollen. This technology enhances public health monitoring by accurately classifying pollen types in urban environments.

Area of Science:

Environmental Science
Biotechnology
Microscopy

Background:

Airborne pollen, a significant aeroallergen, poses public health risks, exacerbated by climate change-induced shifts in pollen dynamics.
Accurate monitoring of airborne pollen is crucial for effective public health interventions and allergy management.
Current methods like bright-field microscopy offer limited detail for precise pollen identification.

Purpose of the Study:

To introduce and evaluate evanescent wave scattering microscopy (EWSM) for label-free, high-resolution identification of airborne pollen.
To develop and implement a two-stage semi-supervised framework for autonomous pollen identification and classification.
To refine the classification system through a human-in-the-loop approach for improved accuracy and scalability.

Main Methods:

Keywords:

ambient pollen bioaerosol monitoring deep learning evanescent wave scattering microscopy (EWSM)fine-grained classification semi-supervised learning

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Published on: December 25, 2017

Utilized evanescent wave scattering microscopy (EWSM) to capture unique scattering signatures of pollen particles, visualizing fine morphological details.
Implemented a two-stage semi-supervised learning framework employing RT-DETR for pollen localization and EfficientNetB0 for classification.
Applied a "human-in-the-loop" strategy for iterative label refinement, enhancing model performance across two phases.

Main Results:

The RT-DETR model achieved an 82.3% recall rate for pollen localization.
Phase 1 refinement improved the F1 score from 0.277 to 0.537 for 41 species, reaching 80% recall.
Phase 2 consolidation into 19 morphological groups yielded a precision of 0.648, recall of 0.744, and F1-score of 0.693, reducing false positives by 63%.

Conclusions:

EWSM provides detailed morphological insights for label-free airborne pollen identification, surpassing traditional microscopy.
The developed semi-supervised framework with human-in-the-loop refinement offers a scalable and accurate solution for autonomous pollen analysis.
This approach holds significant potential for real-time environmental monitoring and public health strategies in urban settings.