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On herbarium specimen images and artificial intelligence.

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Summary

Digitized herbarium specimens offer valuable data for training artificial intelligence (AI) models in plant identification. This approach can augment botanical expertise and enable new research avenues.

Keywords:
artificial intelligenceautomated identificationcomputer visiondataset constructionphenologyplant collections

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Area of Science:

  • Botany
  • Computer Science
  • Artificial Intelligence

Background:

  • Digitized herbarium specimens are increasingly utilized for training artificial intelligence (AI) models.
  • Public repositories offer abundant, standardized specimen images suitable for AI applications.
  • High label accuracy from professional taxonomic review is critical for AI model training.

Purpose of the Study:

  • To review the application of AI computer vision to digitized plant specimens.
  • To outline how AI can be applied to botanical research using herbarium data.
  • To provide guidance on dataset construction and workflow for AI in botany.

Main Methods:

  • Review of AI computer vision techniques applied to herbarium specimen data.
  • Discussion on hypothesis testing and dataset construction for AI botanical research.
  • Development of a general workflow for AI-driven plant identification and research.

Main Results:

  • AI computer vision can effectively utilize digitized herbarium specimens for botanical applications.
  • Standardized specimen data and high label accuracy facilitate AI model development.
  • A structured workflow and best practices are essential for successful AI implementation.

Conclusions:

  • AI-based plant research using digitized herbarium specimens can augment human expertise.
  • This approach offers opportunities for hypothesis-based research in botany.
  • Future AI research can refine models for enhanced botanical applications.