ArticleProceedings of the National Academy of Sciences of the United States of America2020
Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- Automated detection of freeze-thaw signatures in archaeological sediments using deep learning.Open research Europe · 2026Article
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- Digitizing microscope slide-based natural history collections: A protocol using slide scanner technology.PloS one · 2026Article
- Article
- Construction of GAN-RES and Its Application to Small Sample Fusulinid Fossil Recognition.Ecology and evolution · 2025Article
- Barremian tricolpate pollen from Portugal-New evidence for the age of eudicot-related angiosperms.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The evolutionary history evident in grass pollen morphology.The New phytologist · 2025Article
- Pollen image manipulation and projection using latent space.Frontiers in plant science · 2025Article
- PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI.Heliyon · 2024Article
- Automated graptolite identification at high taxonomic resolution using residual networks.iScience · 2024Article
- Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.PloS one · 2024Article
- Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes.PNAS nexus · 2024Article
- Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images.Journal of experimental botany · 2023Article
- Article
- Inferring the age and environmental characteristics of fossil sites using citizen science.PloS one · 2023Article
- Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Imaging plant cells and organs with light-sheet and super-resolution microscopy.Plant physiology · 2022Article
- DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring.The Science of the total environment · 2022Article
- TAIM: Tool for Analyzing Root Images to Calculate the Infection Rate of Arbuscular Mycorrhizal Fungi.Frontiers in plant science · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil
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