Anagun, YildirayIsik, SahinOlgun, MuratUlker, MehmetKoyuncu, OnurDikmen, GokhanBiber, Hanifi2025-12-302025-12-3020251461-41031749-631410.1080/14614103.2025.26001152-s2.0-105025032057https://doi.org/10.1080/14614103.2025.2600115https://hdl.handle.net/20.500.14720/29323This 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.eninfo:eu-repo/semantics/closedAccessAncient WheatArchaeobotanyWheat LandracesConvolutional Neural NetworkScanning Electron MicroscopySurface Texture ClassificationArticleQ4Q2WOS:001640208300001