The Herbarium Challenge 2019 Dataset
This work addresses the need for faster botanical research by providing a dataset to develop automated identification tools for herbarium sheets, which is incremental as it builds on existing computer vision methods.
The authors tackled the problem of automating botanical specimen identification from herbarium sheets by introducing a new dataset of expert-labeled images, as existing datasets focus on wild photos and herbarium sheets present unique challenges.
Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to help experts identify specimens could significantly accelerate research in this area. Whereas most existing botanical datasets comprise photos of specimens in the wild, herbarium sheets exhibit dried specimens, which poses new challenges. We present a challenge dataset of herbarium sheet images labeled by experts, with the intent of facilitating the development of automated identification techniques for this challenging scenario.