# Automated Pollen Recognition in Optical and Holographic Microscopy Images Source: [Automated Pollen Recognition in Optical and Holographic Microscopy Images]() Collection index: [llms.txt]() Data updated: 2025-12-30T05:00:04.958Z Authors: Swarn Singh Warshaneyan, Maksims Ivanovs, Bla\\v{z} Cugmas, Inese B\\=erzi\\c{n}a, Laura Goldberga, Mindaugas Tamosiunas, Roberts Kadi\\c{k}is Published: 2025-12-09T13:31:02.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()