An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network
| dc.authorid | kayci, lokman/0000-0003-4372-5717 | |
| dc.authorid | fidan, mehmet/0000-0002-0255-9727 | |
| dc.contributor.author | Kaya, Yilmaz | |
| dc.contributor.author | Erez, Mehmet Emre | |
| dc.contributor.author | Karabacak, Osman | |
| dc.contributor.author | Kayci, Lokman | |
| dc.contributor.author | Fidan, Mehmet | |
| dc.date.accessioned | 2024-12-24T19:28:01Z | |
| dc.date.available | 2024-12-24T19:28:01Z | |
| dc.date.issued | 2013 | |
| dc.department | Siirt Üniversitesi | |
| dc.description.abstract | Pollen grains vary in colour and shape and can be detected in honey used as a way of identifying nectar sources. Accurate differentiation between pollen grains record is hampered by the combination of poor taxonomic resolution in pollen identification and the high species diversity of many families. Pollen identification determines the origin and the quality of the honey product, but this indefiniteness is also a big challenge for the beekeepers. This study aimed to develop effective, accurate, rapid and non-destructive analysis methods for pollen classification in honey. Ten different pollen grains of plant species were used for the estimation. GLCM (grey level co-occurrence matrix) texture features and ANN (artificial neural network) were used for the identification of pollen grains in honey by the reference of plant species pollen. GLCM has been calculated in four different angles and offsets for the pollen of the plant and the honey samples. Each angle and offset pair includes five features. At the final step, features were classified using the ANN method; the success of estimation with ANN was 88.00%. These findings suggest that the texture parameters can be useful in identification of the pollen types in honey products. | |
| dc.identifier.doi | 10.1080/00173134.2012.754050 | |
| dc.identifier.endpage | 77 | |
| dc.identifier.issn | 0017-3134 | |
| dc.identifier.issn | 1651-2049 | |
| dc.identifier.issue | 1 | |
| dc.identifier.scopus | 2-s2.0-84875952691 | |
| dc.identifier.scopusquality | Q3 | |
| dc.identifier.startpage | 71 | |
| dc.identifier.uri | https://doi.org/10.1080/00173134.2012.754050 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12604/6890 | |
| dc.identifier.volume | 52 | |
| dc.identifier.wos | WOS:000316391700006 | |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Taylor & Francis As | |
| dc.relation.ispartof | Grana | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241222 | |
| dc.subject | Honey | |
| dc.subject | pollen identification | |
| dc.subject | expert system | |
| dc.subject | GLCM | |
| dc.subject | artificial neural network | |
| dc.title | An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network | |
| dc.type | Article |
