Automated Herbarium Specimen Identification using Deep Learning

Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries (Page et al. 2015). Recent initiatives, such as iDigBio (https://www.idigbio.org), aggregate data from and images of vouchered herbarium sheets (and other biocollections) and make this information available to botanists and the general public worldwide through web portals. These ambitious plans to transform and preserve these historical biodiversity data into digital format are supported by the United States National Science Foundation (NSF) Advancing the Digitization of Natural History Collections (ADBC) and the digitization is done by the Thematic Collections Networks (TCNs) funded under the ADBC program. However, thousands of herbarium sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time (Bebber et al. 2010). Computer vision and machine learning approaches applied to herbarium sheets are promising (Wijesingha and Marikar 2012) but are still not well studied compared to automated species identification from leaf scans or pictures of plants taken in the field.