ArticleApplications in plant sciences2020
An algorithm competition for automatic species identification from herbarium specimens.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- A vision of human-AI collaboration for enhanced biological collection curation and research.Bioscience · 2025Article
- Article
- Deep learning for medicinal plant species classification and recognition: a systematic review.Frontiers in plant science · 2023Review
- Getting science priorities straight: how to increase the reliability of specimen identification?Biology letters · 2021Article
- Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.Frontiers in plant science · 2021Article
- The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.Frontiers in plant science · 2021Article
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
- Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots.Applications in plant sciences · 2020Article
- An algorithm competition for automatic species identification from herbarium specimens.Applications in plant sciences · 2020Article
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Authors and funding
10 authors.
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Abstract
premisePlant 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.
methodsTo 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.
resultsIn 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.
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