Next Article in Journal
Beetroot Microencapsulation with Pea Protein Using Spray Drying: Physicochemical, Structural and Functional Properties
Next Article in Special Issue
Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
Previous Article in Journal
Life Cycle Assessment of Different Waste Lubrication Oil Management Options in Serbia
 
 
Article

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

1
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, Greece
2
Department of Agriculture, Hellenic Mediterranean University, 71004 Heraklion, Greece
3
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Minjuan Wang
Appl. Sci. 2021, 11(14), 6657; https://doi.org/10.3390/app11146657
Received: 18 June 2021 / Revised: 14 July 2021 / Accepted: 19 July 2021 / Published: 20 July 2021
(This article belongs to the Special Issue Machine Learning in Agricultural Informatization)
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. View Full-Text
Keywords: dataset; honey; melissopalinology; pollen grain; segmentation dataset; honey; melissopalinology; pollen grain; segmentation
Show Figures

Figure 1

MDPI and ACS Style

Tsiknakis, N.; Savvidaki, E.; Kafetzopoulos, S.; Manikis, G.; Vidakis, N.; Marias, K.; Alissandrakis, E. Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1). Appl. Sci. 2021, 11, 6657. https://doi.org/10.3390/app11146657

AMA Style

Tsiknakis N, Savvidaki E, Kafetzopoulos S, Manikis G, Vidakis N, Marias K, Alissandrakis E. Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1). Applied Sciences. 2021; 11(14):6657. https://doi.org/10.3390/app11146657

Chicago/Turabian Style

Tsiknakis, Nikos, Elisavet Savvidaki, Sotiris Kafetzopoulos, Georgios Manikis, Nikolas Vidakis, Kostas Marias, and Eleftherios Alissandrakis. 2021. "Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)" Applied Sciences 11, no. 14: 6657. https://doi.org/10.3390/app11146657

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop