Potential of SEM and Deep Learning in Archaeobotanical Identification of Ancient Wheat Varieties
| dc.authorscopusid | 55293387500 | |
| dc.authorscopusid | 56247318100 | |
| dc.authorscopusid | 55537456900 | |
| dc.authorscopusid | 8688503700 | |
| dc.authorscopusid | 57112228600 | |
| dc.authorscopusid | 56288729800 | |
| dc.authorscopusid | 57203178520 | |
| dc.contributor.author | Anagun, Yildiray | |
| dc.contributor.author | Isik, Sahin | |
| dc.contributor.author | Olgun, Murat | |
| dc.contributor.author | Ulker, Mehmet | |
| dc.contributor.author | Koyuncu, Onur | |
| dc.contributor.author | Dikmen, Gokhan | |
| dc.contributor.author | Biber, Hanifi | |
| dc.date.accessioned | 2025-12-30T16:05:33Z | |
| dc.date.available | 2025-12-30T16:05:33Z | |
| dc.date.issued | 2025 | |
| dc.department | T.C. Van Yüzüncü Yıl Üniversitesi | en_US |
| dc.department-temp | [Anagun, Yildiray; Isik, Sahin] Eskisehir Osmangazi Univ, Dept Comp Engn, TR-26040 Eskisehir, Turkiye; [Olgun, Murat] Eskisehir Osmangazi Univ, Dept Field Crops, Eskisehir, Turkiye; [Ulker, Mehmet; Ozdemir, Burak; Salih, Sana Jamal; Oral, Erol] Yuzuncu Yil Univ, Fac Agr, Dept Field Crops, Van, Turkiye; [Koyuncu, Onur] Eskisehir Osmangazi Univ, Dept Bot, Eskisehir, Turkiye; [Dikmen, Gokhan] Eskisehir Osmangazi Univ, Cent Res Lab Applicat & Res Ctr ARUM, Eskisehir, Turkiye; [Cavusoglu, Rafet; Biber, Hanifi] Yuzuncu Yil Univ, Dept Archeol, Van, Turkiye; [Altuner, Fevzi] Yuzuncu Yil Univ, Gevas Vocat Sch, Dept Plant & Anim Prod, Van, Turkiye | en_US |
| dc.description.abstract | This study investigated the morphological similarity between bread wheat landraces from the Van Lake Basin and ancient Urartian wheat seeds (9th century BCE) discovered at & Ccedil;avu & scedil;tepe Fortress, utilising a Convolutional Neural Network (CNN)-based framework. Scanning Electron Microscopy (SEM) datasets were created using 15 lines from 10 landraces and the ancient seeds; EfficientNetB0, ResNet18 and InceptionResNetV2 models were employed to extract discriminative surface texture features. The ancient wheat samples showed the highest surface-texture similarity to the Muradiye-1-1 line (Red Kirik wheat) across the tested models (39.8% to 44.1%). These results suggest a phenotypic convergence between ancient and modern landraces under similar agroecological conditions, demonstrating the utility of CNN models for archaeobotanical analysis. | en_US |
| dc.description.sponsorship | Van Yuzuncu Yil University Scientific Research Projects Directorate (Van YYU-SRPD) [FBA 2019-8276] | en_US |
| dc.description.sponsorship | In this study, ancient wheat seeds were used with official permission from the Van Governorship of the Republic of Turkey, the Van Provincial Directorate of Culture and Tourism, and the Van Museum Directorate. Therefore, the dataset is not publicly available. The wheat lines examined in the study were selected from landraces cultivated in the Van Lake Basin, as part of the project titled 'Collection, Identifi-cation, and Preservation of Bread Wheat Landraces Grown in the Van Lake Basin, and Analysis of the Relationships Between Local Varieties and Soil Characteristics.' This project was supported by Van Yuzuncu Yil University Scientific Research Projects Directorate (Van YYU-SRPD, Project No: FBA 2019-8276). The training and validation datasets were prepared and analysed using the scanning electron microscope (SEM) at the Central Research Laboratory Application and Research Center of Eskisehir Osmangazi University. | en_US |
| dc.description.woscitationindex | Science Citation Index Expanded - Arts & Humanities Citation Index | |
| dc.identifier.doi | 10.1080/14614103.2025.2600115 | |
| dc.identifier.issn | 1461-4103 | |
| dc.identifier.issn | 1749-6314 | |
| dc.identifier.scopus | 2-s2.0-105025032057 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https://doi.org/10.1080/14614103.2025.2600115 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14720/29323 | |
| dc.identifier.wos | WOS:001640208300001 | |
| dc.identifier.wosquality | Q4 | |
| dc.language.iso | en | en_US |
| dc.publisher | Routledge Journals, Taylor & Francis Ltd | en_US |
| dc.relation.ispartof | Environmental Archaeology | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Ancient Wheat | en_US |
| dc.subject | Archaeobotany | en_US |
| dc.subject | Wheat Landraces | en_US |
| dc.subject | Convolutional Neural Network | en_US |
| dc.subject | Scanning Electron Microscopy | en_US |
| dc.subject | Surface Texture Classification | en_US |
| dc.title | Potential of SEM and Deep Learning in Archaeobotanical Identification of Ancient Wheat Varieties | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.coar.access | metadata only access | |
| gdc.coar.type | text::journal::journal article |