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Updated: Dec 16, 2025

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
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An algorithm competition for automatic species identification from herbarium specimens.

Damon P Little1, Melissa Tulig1, Kiat Chuan Tan2

  • 1The New York Botanical Garden 2900 Southern Boulevard Bronx New York 10458 USA.

Applications in Plant Sciences
|July 7, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms can accelerate plant species discovery by identifying species from herbarium specimens. The Herbarium 2019 Challenge demonstrated high accuracy in identifying flowering plants, aiding biodiversity research.

Keywords:
FGVCKaggleMelastomataceaeartificial intelligencecomputer visionherbarium specimenmachine learning

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Area of Science:

  • Botany
  • Computer Science
  • Machine Learning

Background:

  • Plant biodiversity faces threats, with many species remaining undescribed.
  • Over 50% of undescribed species are potentially housed in herbaria, awaiting identification.
  • Automatic species identification algorithms can expedite the discovery of new plant species.

Purpose of the Study:

  • To foster the development of automatic species identification algorithms.
  • To leverage machine learning for accelerating species discovery from herbarium collections.
  • To address the challenge of identifying flowering plant species within the Melastomataceae family.

Main Methods:

  • The Herbarium 2019 dataset was submitted to the Fine-Grained Visual Categorization (FGVC6) competition on Kaggle.
  • Focus was placed on the Melastomataceae family, utilizing 46,469 imaged herbarium specimens across 683 species.
  • The dataset included varying numbers of specimens per species, reflecting typical herbarium collection characteristics.

Main Results:

  • The FGVC6 Herbarium 2019 Challenge attracted 22 teams and 254 submitted models.
  • The top-performing algorithms achieved over 88% accuracy in Melastomataceae species identification.
  • The competition facilitated rapid advancements in automated plant identification.

Conclusions:

  • Fine-Grained Visual Categorization competitions offer a valuable platform for computer vision and machine learning experts to tackle species recognition challenges.
  • The Herbarium 2019 Challenge successfully combined herbarium resources, taxonomic knowledge, and interdisciplinary collaboration.
  • This initiative highlights the potential of integrating computational approaches with traditional botanical expertise for biodiversity research.