ArticleApplications in plant sciences
Fully automatic extraction of morphological traits from the web: Utopia or reality?
Article in Applications in plant sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Making floras computable: Audited translation of flora of China as reusable taxonomic knowledge.Plant diversity · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Integrating scanned literature and online sources to build a data text collection for plant biodiversity.Biodiversity data journal · 2026Article
- Learning the syntax of plant assemblages.Nature plants · 2025Article
- Article
- From literature to biodiversity data: mining arthropod organismal traits with machine learning.Biodiversity data journal · 2025Article
- Fully automatic extraction of morphological traits from the web: Utopia or reality?Applications in plant sciencesArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Premise: Plant morphological traits, their observable characteristics, are fundamental to understanding the role played by each species within its ecosystem; however, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, online species descriptions contain massive amounts of information about morphological traits, but the lack of structure makes this source of data impossible to use at scale. Methods: To overcome this, we propose to leverage recent advances in large language models and devise a mechanism for gathering and processing plant trait information in the form of unstructured textual descriptions, without manual curation. Results: We evaluate our approach by automatically replicating three manually created species-trait matrices. Our method found values for over half of all species-trait pairs, with an F1 score of over 75%. Discussion: Our results suggest that large-scale creation of structured trait databases from unstructured online text is now feasible due to the information extraction capabilities of large language models. However, the process is currently limited by the availability of textual descriptions that cover all traits of interest.
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