Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)
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,...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6657 |
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| author | Nikos Tsiknakis Elisavet Savvidaki Sotiris Kafetzopoulos Georgios Manikis Nikolas Vidakis Kostas Marias Eleftherios Alissandrakis |
| author_facet | Nikos Tsiknakis Elisavet Savvidaki Sotiris Kafetzopoulos Georgios Manikis Nikolas Vidakis Kostas Marias Eleftherios Alissandrakis |
| author_sort | Nikos Tsiknakis |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>×</mo><mn>400</mn></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T09:45:38Z |
| format | Article |
| id | doaj.art-0bc8acfac23345a4911c2cc26ffb3ad5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:45:38Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0bc8acfac23345a4911c2cc26ffb3ad52023-11-22T03:13:32ZengMDPI AGApplied Sciences2076-34172021-07-011114665710.3390/app11146657Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)Nikos Tsiknakis0Elisavet Savvidaki1Sotiris Kafetzopoulos2Georgios Manikis3Nikolas Vidakis4Kostas Marias5Eleftherios Alissandrakis6Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, GreeceDepartment of Agriculture, Hellenic Mediterranean University, 71004 Heraklion, GreeceDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, GreeceComputational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, GreeceDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, GreeceComputational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas—FORTH, 70013 Heraklion, GreeceDepartment of Agriculture, Hellenic Mediterranean University, 71004 Heraklion, GreecePollen 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>×</mo><mn>400</mn></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2076-3417/11/14/6657datasethoneymelissopalinologypollen grainsegmentation |
| spellingShingle | Nikos Tsiknakis Elisavet Savvidaki Sotiris Kafetzopoulos Georgios Manikis Nikolas Vidakis Kostas Marias Eleftherios Alissandrakis Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1) Applied Sciences dataset honey melissopalinology pollen grain segmentation |
| title | Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1) |
| title_full | Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1) |
| title_fullStr | Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1) |
| title_full_unstemmed | Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1) |
| title_short | Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1) |
| title_sort | segmenting 20 types of pollen grains for the cretan pollen dataset v1 cpd 1 |
| topic | dataset honey melissopalinology pollen grain segmentation |
| url | https://www.mdpi.com/2076-3417/11/14/6657 |
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