Automated Pollen Classification

Automated Pollen Classification is a computational methodology within palynology that systematically categorizes pollen grains into taxonomic groups using algorithmic analysis of morphological or molecular features, distinguishing it from manual or expert-based identification by its reliance on machine learning, image processing, or artificial intelligence techniques for high-throughput and objective classification.

89

Publications

3.5K

Citations

307

Authors

132

Institutions

Publications per year

2017–2026

49

Authors

307

  1. JR
    J. R. Flenley

    Massey University, New Zealand

    6 publications · 314 citations

    6

    h-index

  2. MP
    Marko Panić

    BioSense Institute, Serbia

    5 publications · 244 citations

    5

    h-index

  3. BŠ
    Branko Šikoparija

    University of Novi Sad, Serbia

    5 publications · 244 citations

    5

    h-index

  4. SW
    Surangi W. Punyasena

    University of Illinois Urbana-Champaign, United States

    5 publications · 235 citations

    5

    h-index

  5. PM
    Predrag Matavulj

    BioSense Institute, Serbia

    5 publications · 244 citations

    5

    h-index

1–5 of 307

Venues

  1. PLoS ONE

    5 publications · 327 citations

    5

    h-index

  2. Review of Palaeobotany and Palynology

    4 publications · 151 citations

    4

    h-index

  3. New Phytologist

    3 publications · 106 citations

    3

    h-index

  4. Aerobiologia

    3 publications · 92 citations

    3

    h-index

  5. Sensors

    2 publications · 64 citations

    3

    h-index

1–5