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., ...More

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

  • Publication Type: Article / Article
  • Publication Date: 2025
  • Doi Number: 10.1080/14614103.2025.2600115
  • Journal Name: ENVIRONMENTAL ARCHAEOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Arts and Humanities Citation Index (AHCI), Scopus, Anthropological Literature, Environment Index, MLA - Modern Language Association Database
  • Keywords: Ancient wheat, archaeobotany, convolutional neural network, scanning electron microscopy, surface texture classification, wheat landraces
  • Eskisehir Osmangazi University Affiliated: Yes

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.