Potential of SEM and Deep Learning in Archaeobotanical Identification of Ancient Wheat Varieties
ENVIRONMENTAL ARCHAEOLOGY, 2025 (SCI-Expanded, AHCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2025
- Doi Numarası: 10.1080/14614103.2025.2600115
- Dergi Adı: ENVIRONMENTAL ARCHAEOLOGY
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Arts and Humanities Citation Index (AHCI), Scopus, Anthropological Literature, Environment Index, MLA - Modern Language Association Database
- Anahtar Kelimeler: Ancient wheat, archaeobotany, convolutional neural network, scanning electron microscopy, surface texture classification, wheat landraces
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
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.