Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.

Abstract

Herbarium specimens are physical, verifiable records that form the basis of taxonomic knowledge and biodiversity research. Their large-scale digitization has produced extensive collections of high-resolution images and associated specimen metadata, creating conditions in which artificial intelligence (AI) can play an important role in plant taxonomy, collection management, and ecological research. Early AI applications have primarily focused on automated species identification based on individual specimen images. Although increasingly accurate, such approaches remain limited by their emphasis on single-specimen label prediction and by treating identification outputs as final analytical decisions. Recent methodological advances-including segmentation-based preprocessing, automated trait extraction, structured extraction of label data, detection of potentially misidentified specimens, and multimodal integration of visual, textual, and genetic information-extend AI applications beyond species identification toward broader analytical frameworks, encompassing taxonomic interpretation as well as ecological and biodiversity research. In these approaches, specimens are placed within a shared analytical space, and identification results are used to support comparisons across multiple specimens rather than being treated as final decisions for single individuals. This multi-specimen perspective enables quantitative examination of species boundaries, morphological variation, data inconsistencies, and taxonomic stability within curated collections. In this context, AI serves not as an ultimate decision-maker but as a decision-support tool embedded in expert-guided workflows and biodiversity knowledge infrastructures. These developments can be summarized as Integrative Taxonomic AI, an approach that employs learned morphospaces to interpret and refine taxonomic categories by integrating multimodal evidence and curated specimen data under expert guidance.

Plant phenotyping relevance

植物標本画像から形態形質を抽出するAI手法と統合的解析枠組みを中心に扱うレビューであり、植物表現型取得・抽出法との関連が明確。

Code and data availability

This is a review article on AI for herbarium specimens. All cited datasets, portals, and tools (iDigBio, AVH, CVH, Herbarium Challenge, LeafMachine, etc.) are prior work or generic repositories, not assets reproducing this paper's own phenotyping measurements or analysis. No author code, data, or model availability/de-

No evidence-backed public reproduction asset is currently recorded.