Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning.

Journal: IEEE transactions on bio-medical engineering
Published Date: Jan 20, 2022

Abstract

OBJECTIVE: In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches.

Authors

  • Michele DaOrazio
  • Riccardo Reale
    Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy.
  • Adele De Ninno
    Institute for Photonics and Nanotechnology, Italian National Research Council, Rome, Italy.
  • Maria A Brighetti
  • Arianna Mencattini
    Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy.
  • Luca Businaro
  • Eugenio Martinelli
    Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy.
  • Paolo Bisegna
    Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy.
  • Alessandro Travaglini
  • Federica Caselli
    Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy.

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