Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – establishment of an automated multi-taxa pollen counting estimation system (AME system)

Shigeto Kawashima,Kenji Miki

doi:10.5194/amt-14-685-2021

Abstract

Abstract. Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination. Our experimental results indicate that different pollen types often have different light scattering characteristics, as previous research has suggested. Our results also show that light scattering distributions did not undergo significant temporal changes. Further, we show that the concentration of two different types of pollen could be estimated separately from the total number of pollen grains by fitting the light scattering data to a probability density curve. These findings should help realize a fast and simple automatic pollen monitoring system.

Highlights

Pollen counting is a time-consuming and labor-intensive task that requires professional skills
Light scattering data from various pollen taxa indicate that it is not possible to discriminate between the side scattering patterns of Alnus vs. Ambrosia, Alnus vs. Corylus, Alnus vs. Olea, Ambrosia vs. Fraxinus, Betula vs. Phleum, Betula vs. Quercus, Corylus vs. Olea, Fagus vs. Zea, Artemisia vs. Fraxinus, Helianthus vs. Zea, and Phleum vs. Quercus and the forward scattering patterns between Alnus vs. Corylus, Alnus vs. Quercus, Ambrosia vs. Artemisia, Ambrosia vs. Fraxinus, Artemisia vs. Fraxinus, Betula vs. Phleum, Betula vs. Quercus, Castanea vs. Olea, Cedrus vs. Helianthus, Corylus vs. Quercus, Fagus vs. Helianthus, Fagus vs. Zea, and Phleum vs. Quercus, all of which show similar scattering intensities
We found average errors of 20 %–40 % for Alnus and Artemisia, values which are likely applicable to other taxa such as Cryptomeria japonica

Summary

Introduction

Pollen counting is a time-consuming and labor-intensive task that requires professional skills. Many studies applied machine learning algorithms to the problem (Punyasena et al, 2012; Tcheng et al, 2016; Crouzy et al, 2016; Gonçalves et al, 2016; Gallardo-Caballero et al, 2019; Šaulieneet al., 2019). Studies applying machine learning algorithms have shown that light scattering patterns can be used for automatic classification and counting of multiple pollen taxa simultaneously (Crouzy et al, 2016; Sauliene et al, 2019). Other studies have applied statistical techniques to compare the light scattering data and number of multiple taxa pollen grains (Kawashima et al, 2007, 2017; Matsuda and Kawashima, 2018). Surbek et al (2011) studied the discrimination method for hazel, birch, willow, ragweed, and pine pollen showing that they have distinct

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