# Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset V1 (CPD-1)
## 1 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. Pollen analysis, also known as melissospalynology, is a method to determine the origin of honey, based on the determination of the pollen grains presented in it [2]. Honey contains pollen as a result of the manipulation of the flower when honeyes collect nectar. The procedure largely followed today is based on the method described in [3]. Although 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, thus, 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.  
The automation of this process could work directly, reducing the analysis time, whilst increasing the accuracy of the results. Procedures related to image analysis and the use of neural networks have yielded significant results. Recent years have seen advances in the fields of machine learning and artificial intelligence related to pollen analysis [5], with quite impressive results with up to 98% correct pollen classification [6].  
Geographical indications (GIs) identify a good honey as one that originates from a particular place, has a particular quality, characteristic, or reputation [7]. In most cases, these indications have a determinative role in both domestic and international markets [8].  
One such product is \"Perfkothymaromelo Kritis PDO\", a honey specific to Crete, which is characterized by specific organoleptic, physicochemical, and microscopic characteristics. To certify this product, pollen analysis is crucial. The main and defining type of pollen in that particular honey is thyme (formerly called _Coridothymus capitatus_, currently referred to as _Thymbra capitata_) which is present in all honey samples in a percentage greater than or equal to 10%. In addition, another 15 to 20 types of pollen grains from different plants are indicative of Cretan nature, mainly chestnut (_Castanea sativa_), heather (_Erica arborea_, _Erica manipiflora_), eucalyptus (_Eucalyptus cumaldulensis_), myrtle (_Myrtus communuis_), different species of _Sinapis_ sp. (Brassicaceae), carob (_Creatonia siliqua_), giant fennel (_Ferula communuis_) usage (_Salvia officinalis_), marjoram (_Origanum microphyllum_), and savory (_Satureja thymbra_) [9]. Therefore, an automated process of microscopic identification could aid the fast and accurate quality control of this product.  
For this reason, a collection of pollen grains from plants usually present in \"Perfkothymaromelo Kritis, PDO\" was developed to create a microscopic imaging dataset. In particular, the scope of this work was to gather, prepare, and capture such images, as well as to develop an image processing method to segment each unique pollen grain. Doing so, the analysis of each pollen grain becomes easier, also enabling machine learning classification methods to be developed. The dataset is also publicly available at 10.5281/zenodo.4756360 (accessed on 13 May 2021) under a CC BY 4.0 licence for other researchers to use [10].  
The rest of the article is structured as follows. Section 2 presents a short overview of pollen grain segmentation methods. Section 3 presents the data acquisition, quality control and annotation protocol. The segmentation pipeline, as well as the performance results of the method are presented in Sections 4 and 5, respectively. Finally, the findings of the study are discussed in Section 6 and the article is concluded in Section 7.