What question did this study set out to answer?

To enhance pollen recognition in veterinary imaging using AI techniques for optical and holographic microscopy.

March 28, 2026Open Access

AI-augmented pollen recognition in optical and holographic microscopy for veterinary imaging

Key Points

To enhance pollen recognition in veterinary imaging using AI techniques for optical and holographic microscopy.
Train YOLOv8s for object detection and MobileNetV3L for classification.
Use a dual-modality dataset of annotated optical and DIHM images.
Employ a Wasserstein GAN with spectral normalization for synthetic image generation.
Test detection and classification accuracy on both optical and DIHM images.
Detection mAP50 for optical images reached 91.3% with 97% classification accuracy.
DIHM detection mAP50 was 8.15%, and classification accuracy was 50%.
Using GAN-generated images improved DIHM detection by up to 15.4%.
Mixing real and synthetic data enhanced accuracy for DIHM images.

Abstract

Abstract We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appearances. We establish the performance baseline by training YOLOv8s for object detection and MobileNetV3L for classification on a dual-modality dataset of automatically annotated optical and affinely aligned DIHM images. On optical data, detection mAP50 reaches 91.3% and classification accuracy reaches 97%, whereas on DIHM data, we achieve only 8.15% for detection mAP50 and 50% for classification accuracy. Expanding the bounding boxes of pollens in DIHM images over those acquired in aligned optical images achieves 13.3% for detection mAP50 and 54% for classification accuracy. To improve object detection in DIHM images, we employ a Wasserstein GAN with spectral normalization (WGAN-SN) to create synthetic DIHM images, yielding an FID score of 58.246. Mixing real-world and synthetic data at the 1.0 : 1.5 ratio for DIHM images improves object detection up to 15.4%. These results demonstrate that GAN-based augmentation can reduce the performance divide, bringing fully automated DIHM workflows for veterinary imaging a small but important step closer to practice.

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