ArticleApplications in plant sciences
From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2.
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 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Manage your image database with computer vision-Lessons learned at Encyclopedia of Life.Bioscience · 2026Article
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Automated extraction of leaf mass per area from digitized herbarium specimens.The New phytologist · 2026Article
- Herbarium specimens reveal drivers of Arctic shrub growth.The New phytologist · 2026Article
- National biodiversity data infrastructures: ten essential functions for science, policy, and practice.Bioscience · 2025Article
- A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation inBioinformatics and biology insights · 2025Article
- Geographical-Historical Analysis of the Herbarium Specimens Representing the Economically Important FamilyBiology · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- From Dormant Collections to Repositories for the Study of Habitat Changes: The Importance of Herbaria in Modern Life Sciences.Life (Basel, Switzerland) · 2023Review
- Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation.Applications in plant sciencesArticle
- PlantSAM: An object detection-driven segmentation pipeline for herbarium specimens.Applications in plant sciencesArticle
- FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes.Applications in plant sciencesArticle
- From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2.Applications in plant sciencesArticle
- mvh: An R tool to assemble and organize virtual herbaria from openly available specimen images.Applications in plant sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Premise: Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets. Methods: LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms. Results: LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types. Discussion: LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.