ArticleApplications in plant sciences2020
LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Herbarium-based measurements are reliable predictors of fresh plant traits in Neotropical Myrtaceae.Applications in plant sciences · 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
- The promise of digital herbarium specimens in large-scale phenology research.The New phytologist · 2026Review
- From Knowledge to Action: Next Steps for the Natural Science Collections Community.Bioscience · 2026Article
- Genomic resources for crop wild relatives are critical for perennial fruit breeding and conservation.American journal of botany · 2025Review
- A vision of human-AI collaboration for enhanced biological collection curation and research.Bioscience · 2025Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Geometric Morphometric Versus Genomic Patterns in a Large Polyploid Plant Species Complex.Biology · 2023Article
- Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study ofPlants (Basel, Switzerland) · 2021Article
- Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries.Biodiversity data journal · 2021Article
- Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.Frontiers in plant science · 2021Article
- An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning.PhytoKeys · 2021Article
- Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.Applications in plant sciences · 2020Article
- GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens.Applications in plant sciences · 2020Article
- A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.Applications in plant sciences · 2020Article
- LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens.Applications in plant sciences · 2020Article
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3 authors.
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Abstract
premiseObtaining phenotypic data from herbarium specimens can provide important insights into plant evolution and ecology but requires significant manual effort and time. Here, we present LeafMachine, an application designed to autonomously measure leaves from digitized herbarium specimens or leaf images using an ensemble of machine learning algorithms. METHODS AND
resultsWe trained LeafMachine on 2685 randomly sampled specimens from 138 herbaria and evaluated its performance on specimens spanning 20 diverse families and varying widely in resolution, quality, and layout. LeafMachine successfully extracted at least one leaf measurement from 82.0% and 60.8% of high- and low-resolution images, respectively. Of the unmeasured specimens, only 0.9% and 2.1% of high- and low-resolution images, respectively, were visually judged to have measurable leaves.
conclusionsThis flexible autonomous tool has the potential to vastly increase available trait information from herbarium specimens, and inform a multitude of evolutionary and ecological studies.
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