ArticleBMC evolutionary biology2016
Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification.
Article in BMC evolutionary biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in Plant Phenological Research: A Systematic Literature Review.Frontiers in plant science · 2022Pooled it
- A vision of human-AI collaboration for enhanced biological collection curation and research.Bioscience · 2025Article
- Exploring the landscape of automated species identification apps: Development, promise, and user appraisal.Bioscience · 2024Review
- Identification of herbarium specimen sheet components from high-resolution images using deep learning.Ecology and evolution · 2023Article
- Deep learning image segmentation reveals patterns of UV reflectance evolution in passerine birds.Nature communications · 2022Article
- The Warps and Wefts of a Polyploidy Complex: Integrative Species Delimitation of the DiploidPlants (Basel, Switzerland) · 2022Article
- Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest.Plants (Basel, Switzerland) · 2022Article
- Research on Face Recognition Algorithm Based on Image Processing.Computational intelligence and neuroscience · 2022Article
- The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.Frontiers in plant science · 2021Article
- Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.Bioscience · 2020Article
- LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens.Applications in plant sciences · 2020Article
- Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning.Applications in plant sciences · 2020Article
- An algorithm competition for automatic species identification from herbarium specimens.Applications in plant sciences · 2020Article
- GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens.Applications in plant sciences · 2020Article
- A method to implement continuous characters in digital identification keys that estimates the probability of an annotation.Applications in plant sciences · 2019Article
- Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras.Applications in plant sciences · 2019Article
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.GigaScience · 2019Review
- Using herbaria to study global environmental change.The New phytologist · 2019Article
- Automated plant species identification-Trends and future directions.PLoS computational biology · 2018Review
- Going deeper in the automated identification of Herbarium specimens.BMC evolutionary biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlobal Plants, a collaborative between JSTOR and some 300 herbaria, now contains about 2.48 million high-resolution images of plant specimens, a number that continues to grow, and collections that are digitizing their specimens at high resolution are allocating considerable recourses to the maintenance of computer hardware (e.g., servers) and to acquiring digital storage space. We here apply machine learning, specifically the training of a Support-Vector-Machine, to classify specimen images into categories, ideally at the species level, using the 26 most common tree species in Germany as a test case.
resultsWe designed an analysis pipeline and classification system consisting of segmentation, normalization, feature extraction, and classification steps and evaluated the system in two test sets, one with 26 species, the other with 17, in each case using 10 images per species of plants collected between 1820 and 1995, which simulates the empirical situation that most named species are represented in herbaria and databases, such as JSTOR, by few specimens. We achieved 73.21% accuracy of species assignments in the larger test set, and 84.88% in the smaller test set.
conclusionsThe results of this first application of a computer vision algorithm trained on images of herbarium specimens shows that despite the problem of overlapping leaves, leaf-architectural features can be used to categorize specimens to species with good accuracy. Computer vision is poised to play a significant role in future rapid identification at least for frequently collected genera or species in the European flora.
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.