Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)

Nikos Tsiknakis,Kostas Marias,Elisavet Savvidaki,Nikolas Vidakis,Sotiris Kafetzopoulos,Eleftherios Alissandrakis,Georgios Manikis

doi:10.3390/app11146657

Abstract

Pollen analysis and the classification of several pollen species is an important task in melissopalynology. The development of machine learning or deep learning based classification models depends on available datasets of pollen grains from various plant species from around the globe. In this paper, Cretan Pollen Dataset v1 (CPD-1) is presented, which is a novel dataset of grains from 20 pollen species from plants gathered in Crete, Greece. The pollen grains were prepared and stained with fuchsin, in order to be captured by a camera attached to a microscope under a ×400 magnification. In addition, a pollen grain segmentation method is presented, which segments and crops each unique pollen grain and achieved an overall detection accuracy of 92%. The final dataset comprises 4034 segmented pollen grains of 20 different pollen species, as well as the raw data and ground truth, as annotated by an expert. The developed dataset is publicly accessible, which we hope will accelerate research in melissopalynology.

Highlights

Honey is a natural food with great importance for many countries due to its nutritional and medicinal properties [1]
Known as melissopalynology, is a method to determine the origin of honey, based on the determination of the pollen grains presented in it [2]
We present a novel pipeline for segmenting pollen grains from fuchsinstained microscope images of 20 types of plants gathered in Crete, Greece

Summary

Introduction

Honey is a natural food with great importance for many countries due to its nutritional and medicinal properties [1]. It is highly appreciated by consumers and the demand for honey of certified origin is currently a necessity. The procedure largely followed today is based on the method described in [3]. Widely accepted, this method has significant disadvantages, namely the high manual effort and the specialized expertise required, both leading to high costs and high demands on time and, to restrictions in sample throughput [4]. Because of the high financial importance of honey, it is necessary to provide effective tools that can help ensure its origin

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Conclusion