<scp>AI</scp>‐based identification of Chinese vascular plants from herbarium specimens: a tool for all herbaria with Chinese holdings

Documenting the geographic ranges of the World's > 350 000 named species of vascular plants requires artificial intelligence (AI) because there are insufficient experts to speedily identify dried plants, a task that requires access to thousands of already named specimens representing the applicable names. Focussing on herbarium material, we built an AI model that helps identify China's vascular plants. Training and testing sets consisted of images representing 21 589 species (including 1866 infraspecific taxa), 3144 genera, and 309 families of vascular plants represented in Chinese herbaria that have digitized over 6.5 million images of herbarium specimens identified to species level. The resulting AI model is able to identify all 21 589 species, covering an estimated 53.9% of China's vascular plant diversity. A test of the model with 244 749 expert-identified specimen images representing the species resulted in a species-level accuracy of 67.3% for Top-1 and 88.2% for Top-5. Our '21Ksp' model is freely accessible at https://www.iplant.cn/stu/spm. The new model, and a related tool with identified live photographs of 20 000 vascular species common in China (FlowerMate 2.0, https://www.iplant.cn/stu/20k), will help reduce the backlog of unidentified specimens in hundreds of herbaria world-wide that harbour collections from China and adjacent countries.

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Publication Details

Journal
New Phytologist
Published
2025-08-09
DOI
https://doi.org/10.1111/nph.70443
Citations
1
Primary Topic
Plant and animal studies
Type
article
Field-Weighted Citation Impact
3.03
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article

AI‐based identification of Chinese vascular plants from herbarium specimens: a tool for all herbaria with Chinese holdings

Jing Xuan, Susanne S. Renner, Min Li, Gan Xie et al.
1 citations
New Phytologist
Plant and animal studies
3.03
article

AI‐based identification of Chinese vascular plants from herbarium specimens: a tool for all herbaria with Chinese holdings

Jing Xuan, Susanne S. Renner, Min Li, Gan Xie, Hui‐Yuan Liu, Haibo Luo
article en
1 citations

Abstract

Documenting the geographic ranges of the World's > 350 000 named species of vascular plants requires artificial intelligence (AI) because there are insufficient experts to speedily identify dried plants, a task that requires access to thousands of already named specimens representing the applicable names. Focussing on herbarium material, we built an AI model that helps identify China's vascular plants. Training and testing sets consisted of images representing 21 589 species (including 1866 infraspecific taxa), 3144 genera, and 309 families of vascular plants represented in Chinese herbaria that have digitized over 6.5 million images of herbarium specimens identified to species level. The resulting AI model is able to identify all 21 589 species, covering an estimated 53.9% of China's vascular plant diversity. A test of the model with 244 749 expert-identified specimen images representing the species resulted in a species-level accuracy of 67.3% for Top-1 and 88.2% for Top-5. Our '21Ksp' model is freely accessible at https://www.iplant.cn/stu/spm. The new model, and a related tool with identified live photographs of 20 000 vascular species common in China (FlowerMate 2.0, https://www.iplant.cn/stu/20k), will help reduce the backlog of unidentified specimens in hundreds of herbaria world-wide that harbour collections from China and adjacent countries.

New Phytologist
Washington University in St. Louis (US), Beijing Botanical Garden (CN), Institute of Botany of the Slovak Academy of Sciences (SK), Zero to Three (US)
Life below water
Openalex Percentile: Top 9%
Plant and animal studies
3.03
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<scp>AI</scp>‐based identification of Chinese vascular plants from herbarium specimens: a tool for all herbaria with Chinese holdings — Jing Xuan, Susanne S. Renner, et al. · New Phytologist (2025) | TGRS Research Map | TGRS