Automatic Extraction of Specimens from Multi-specimen Herbaria

Since herbarium specimens are increasingly becoming digitized and accessible in online repositories, an important need has emerged to develop automated tools to process and enrich these collections to facilitate better access to the preserved archives. Particularly, automatic enrichment of multi-specimen herbaria sheets poses unique challenges and problems that have not been adequately addressed. The complexity of localization of species in a page increases exponentially when multiple specimens are present in the same page. This already challenges the performance of models that work accurately with single specimens. Therefore, in this work, we have performed experiments to identify the models that perform well for the plant specimen localization problem. The major bottleneck for performing such experiments was the lack of labeled data. We also address this problem by proposing tools and algorithms to semi-automatically generate annotations for herbarium images. Based on our experiments, segmentation models perform much better than detection models for the task of plant localization. Our binary segmentation model can accurately extract specimens from the background and achieves an F1 score of 0.977. The ablation experiments for multi-specimen instance segmentation show that our proposed augmentation method provides a 38% increase in performance (0.51 mAP@0.9 versus 0.37) on a dataset of 1,500 plant instances.

Automated summary

With the increasing availability of digitized herbarium specimens in online repositories, the development of automated tools to process and enrich these collections has become crucial for better access to preserved archives. The automatic enrichment of multi-specimen herbaria sheets presents unique challenges, but in this study, experiments were conducted to identify effective models for plant specimen localization. The lack of labeled data was addressed by proposing tools and algorithms to semi-automatically generate annotations for herbarium images. The results showed that segmentation models performed better than detection models, achieving an F1 score of 0.977 for accurately extracting specimens from the background.