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<p>The digitisation of hundreds of millions of herbarium specimen images and their labels has created an unprecedented resource for taxonomy, ecology, and conservation, motivating the development of artificial intelligence (AI) solutions. Automated analysis of these high-resolution scans faces significant challenges, including data imbalance, information loss, model interpretability and explainability, and scalable Open-Set Recognition (OSR). This paper provides an in-depth algorithm-level review of AI methodologies for herbarium image classification, tracing the development from classical classification models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to cutting-edge multimodal frameworks. In addition to classification, the review further investigates vision-based analytical tasks critical to herbarium image analysis, including specimen image segmentation, label text identification using Large Language Models (LLMs), and Human-in-the-Loop (HITL) quality assurance strategies. Furthermore, this review reveals practical challenges in specimen image analysis along with their promising solutions and potential future directions.</p>

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A review of artificial intelligence in herbarium specimen image analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月31日

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  • Yu-Yue Guo,
  • Haibin Cai,
  • Gemma L. C. Bramley,
  • Hannah J. Atkins,
  • Baihua Li,
  • Stephanos Theodossiades

摘要

The digitisation of hundreds of millions of herbarium specimen images and their labels has created an unprecedented resource for taxonomy, ecology, and conservation, motivating the development of artificial intelligence (AI) solutions. Automated analysis of these high-resolution scans faces significant challenges, including data imbalance, information loss, model interpretability and explainability, and scalable Open-Set Recognition (OSR). This paper provides an in-depth algorithm-level review of AI methodologies for herbarium image classification, tracing the development from classical classification models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to cutting-edge multimodal frameworks. In addition to classification, the review further investigates vision-based analytical tasks critical to herbarium image analysis, including specimen image segmentation, label text identification using Large Language Models (LLMs), and Human-in-the-Loop (HITL) quality assurance strategies. Furthermore, this review reveals practical challenges in specimen image analysis along with their promising solutions and potential future directions.