ArticleFrontiers in plant science2021
The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- The promise of digital herbarium specimens in large-scale phenology research.The New phytologist · 2026Review
- Advancing plant DNA barcoding: integrating chloroplast genome sequencing, cryptic diversity discovery and machine learning.Molecular biology reports · 2026Review
- Development of a system for the automated identification of herbarium specimens with high accuracy.Scientific reports · 2022Article
- Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.Frontiers in plant science · 2021Article
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18 authors.
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Abstract
Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all-important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine-grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half-Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half-Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.
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