An algorithm competition for automatic species identification from herbarium specimens

Autor: Barbara A. Ambrose, Serge Belongie, Damon P. Little, Melissa Tulig, Fabián A. Michelangeli, Ramanathan V. Guha, Christine Kaeser-Chen, Yulong Liu, Kiran S. Panesar, Kiat Chuan Tan
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Applications in Plant Sciences, Vol 8, Iss 6, Pp n/a-n/a (2020)
Applications in Plant Sciences
ISSN: 2168-0450
Popis: Premise Plant biodiversity is threatened, yet many species remain undescribed. It is estimated that >50% of undescribed species have already been collected and are awaiting discovery in herbaria. Robust automatic species identification algorithms using machine learning could accelerate species discovery. Methods To encourage the development of an automatic species identification algorithm, we submitted our Herbarium 2019 data set to the Fine-Grained Visual Categorization sub-competition (FGVC6) hosted on the Kaggle platform. We chose to focus on the flowering plant family Melastomataceae because we have a large collection of imaged herbarium specimens (46,469 specimens representing 683 species) and taxonomic expertise in the family. As is common for herbarium collections, some species in this data set are represented by few specimens and others by many. Results In less than three months, the FGVC6 Herbarium 2019 Challenge drew 22 teams who entered 254 models for Melastomataceae species identification. The four best algorithms identified species with >88% accuracy. Discussion The FGVC competitions provide a unique opportunity for computer vision and machine learning experts to address difficult species-recognition problems. The Herbarium 2019 Challenge brought together a novel combination of collections resources, taxonomic expertise, and collaboration between botanists and computer scientists.
Databáze: OpenAIRE
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