ArticlePloS one2020
Precise automatic classification of 46 different pollen types with convolutional neural networks.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Efficient Automatic Pollen Recognition From Fossil Pollen Samples: A High-Resolution Example Record From Palaeolake Kieshofer Moor, Northeastern Germany.Ecology and evolution · 2026Article
- Method comparison of microscopy, metabarcoding, and multispectral imaging flow cytometry for identification and relative abundance analysis of insect-dispersed pollen.Scientific reports · 2026Article
- Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment.Frontiers in allergy · 2026Article
- Efficient and scalable training set generation for automated pollen monitoring with Hirst-type samplers.Scientific reports · 2025Article
- Pollen image manipulation and projection using latent space.Frontiers in plant science · 2025Article
- Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology.Frontiers in big data · 2025Article
- Pollen Grain Classification Using Some Convolutional Neural Network Architectures.Journal of imaging · 2024Article
- Automated classification of pollen grains microscopic images using cognitive attention based on human Two Visual Streams Hypothesis.PloS one · 2024Article
- Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.PloS one · 2024Article
- Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.The New phytologist · 2023Article
- Article
- Detecting Airborne Pollen Using an Automatic, Real-Time Monitoring System: Evidence from Two Sites.International journal of environmental research and public health · 2022Article
- Imaging Flow Cytometry as a Quick and Effective Identification Technique of Pollen Grains fromCells · 2022Article
- DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring.The Science of the total environment · 2022Article
- Discrimination of pollen of New Zealand mānuka (Leptospermum scoparium agg.) and kānuka (Kunzea spp.) (Myrtaceae).PloS one · 2022Article
- Neural networks for increased accuracy of allergenic pollen monitoring.Scientific reports · 2021Article
- Deep Learning Methods for Improving Pollen Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable.
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