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Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey

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Book cover Signal and Image Analysis for Biomedical and Life Sciences

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 823))

Abstract

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

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Authors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Locked Bag 17, North Ryde, Sydney, NSW, 1670, Australia

    Ryan Lagerstrom, Yulia Arzhaeva & Leanne Bischof

  2. Institute of Natural Resources, Massey University, PB 11222, Palmerston North, 4442, New Zealand

    Katherine Holt

  3. School of Culture, History and Language, H C Coombs Blg 9, The Australian National University, Canberra, ACT, 0200, Australia

    Simon Haberle & Felicitas Hopf

  4. Digital Productivity Flagship, CSIRO, GPO Box 664, Canberra, ACT, 2601, Australia

    David Lovell

Authors
  1. Ryan Lagerstrom
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  2. Katherine Holt
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  3. Yulia Arzhaeva
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  4. Leanne Bischof
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  5. Simon Haberle
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  6. Felicitas Hopf
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  7. David Lovell
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Corresponding author

Correspondence to Ryan Lagerstrom .

Editor information

Editors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Changming Sun

  2. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Tomasz Bednarz

  3. The University of Aizu, Fukushima, Japan

    Tuan D. Pham

  4. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Pascal Vallotton

  5. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Dadong Wang

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Lagerstrom, R. et al. (2015). Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey. In: Sun, C., Bednarz, T., Pham, T., Vallotton, P., Wang, D. (eds) Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-10984-8_12

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