Potential of SEM and Deep Learning in Archaeobotanical Identification of Ancient Wheat Varieties

ANAGÜN Y., IŞIK Ş., OLGUN M., Ülker M., KOYUNCU O., DİKMEN G., ...Daha Fazla

ENVIRONMENTAL ARCHAEOLOGY, 2025 (SCI-Expanded, AHCI, Scopus) identifier identifier

  • 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.