Pollen-YOLO: A Deep Learning Framework for Automated Pollen Identification and Its Application to Palaeoecological Reconstruction on the Tibetan Plateau

Automated pollen identification has become an increasingly important tool for palaeoecological research; however, its application to fossil pollen assemblages remains challenging due to complex backgrounds, morphological variability, and taxonomic similarity among pollen types. In this study, we propose Pollen-YOLO, a deep learning-based object detection framework designed for automated pollen identification from microscopic images, and evaluate its performance using the TPPOL23 dataset. The model integrates a tailored backbone architecture with attention-based feature enhancement and class-specific data augmentation strategies to address the characteristics of fossil pollen images. Experimental results indicate that Pollen-YOLO achieves stable and competitive detection performance for most pollen taxa under the tested conditions, particularly for dominant taxa with distinctive morphological features. Model behavior is further examined through ablation experiments and Grad-CAM-based interpretability analysis, which provide insights into feature learning and classification mechanisms. The applicability of the framework is explored using a fossil pollen sequence from the Shaqu profile on the Tibetan Plateau. Automated results show a high level of agreement with manual identification in capturing major stratigraphic trends and vegetation succession patterns, while discrepancies persist for morphologically similar or low-abundance taxa. Overall, this study suggests that object detection-based deep learning approaches have the potential to support fossil pollen analysis and palaeoecological reconstruction. Rather than replacing expert identification, Pollen-YOLO is intended as a complementary, high-throughput tool that may assist large-scale pollen analysis under appropriate quality control when combined with expert verification.