Precise automatic classification of 46 different pollen types with convolutional neural networks

Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks

(2018) Víctor Sevillano et al.   PLoS One

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

(2017) Katharina Bastl et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains

(2016) Ariadne Barbosa Gonçalves et al.   PLoS One

Automated pollen identification using microscopic imaging and texture analysis

(2015) J. Víctor Marcos et al.   MICRON

Principles and methods for automated palynology

(2014) K. A. Holt et al.   NEW PHYTOLOGIST

An expert classification system of pollen of Onopordum using a rough set approach

(2012) Yılmaz Kaya et al.   REVIEW OF PALAEOBOTANY AND PALYNOLOGY

Rapid climate change from north Andean Lake Fúquene pollen records driven by obliquity: implications for a basin-wide biostratigraphic zonation for the last 284 ka

(2011) R.G. Bogotá-A et al.   QUATERNARY SCIENCE REVIEWS

Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory

(2011) K. Holt et al.   REVIEW OF PALAEOBOTANY AND PALYNOLOGY

Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning

(2009) R. Dell’Anna et al.   ANALYTICAL AND BIOANALYTICAL CHEMISTRY

Classification of pollen species using autofluorescence image analysis

(2009) Kotaro Mitsumoto et al.   JOURNAL OF BIOSCIENCE AND BIOENGINEERING