Automated Pollen Recognition in Optical and Holographic Microscopy Images

arXiv:2512.08589 · cs.CV, cs.LG, q-bio.QM, stat.ML · Submitted 2025-12-09 · Read on arXiv

cs.CV, cs.LG, q-bio.QM, stat.ML

Submitted: 2025-12-09

Updated: 2025-12-09

Comments: 08 pages, 10 figures, 04 tables, 20 references. Date of Conference: 13-14 June 2025 Date Added to IEEE Xplore: 10 July 2025 Electronic ISBN: 979-8-3315-0969-9 Print on Demand(PoD) ISBN: 979-8-3315-0970-5 DOI: 10.1109/AICCONF64766.2025.11064260 Conference Location: Prague, Czech Republic Online Access: https://ieeexplore.ieee.org/document/11064260

Journal ref: 2025 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF), Prague, Czech Republic, pp. 1-8 (2025)

DOI: 10.1109/AICCONF64766.2025.11064260

Code: https://github.com/ultralytics/ultralytics

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on

Terminology

Accuracy — 17× in this paper · explained in 1 episode
Transfer Learning — 5× in this paper · explained in 10 episodes
Alignment — 3× in this paper · explained in 1 episode
Feature Extraction — 3× in this paper · explained in 2 episodes
Loss Function — 3× in this paper · explained in 2 episodes
Edge Devices — 2× in this paper · explained in 2 episodes
Fine-Tuning — 2× in this paper · explained in 5 episodes
Adam — 1× in this paper · explained in 2 episodes
Adaptation — 1× in this paper · explained in 1 episode
Composite Loss Function — 1× in this paper · explained in 2 episodes
Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
Domain Adaptation — 1× in this paper · explained in 5 episodes
Domain Shift — 1× in this paper · explained in 4 episodes

Abstract

This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use cases. We used YOLOv8s for object detection and MobileNetV3L for the classification task, evaluating their performance across imaging modalities. The models achieved 91.3% mAP50 for detection and 97% overall accuracy for classification on optical images, whereas the initial performance on greyscale holographic images was substantially lower. We addressed the performance gap issue through dataset expansion using automated labeling and bounding box area enlargement. These techniques, applied to holographic images, improved detection performance from 2.49% to 13.3% mAP50 and classification performance from 42% to 54%. Our work demonstrates that, at least for image classification tasks, it is possible to pair deep learning techniques with cost-effective lensless digital holographic microscopy devices.

Terminology

Abstract

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