BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8.

Accurate classification of cherry varieties is crucial for their economic value and market differentiation, yet their genetic diversity and visual similarity make manual identification challenging, hindering efficient agricultural and trade practices. This study addresses this issue by proposing a novel deep learning-based hybrid model that integrates BiFPN with the YOLOv8n-cls framework, enhanced by Swin Transformer and Deformable Attention Transformer (DAT) techniques. The model was trained and evaluated on a newly constructed dataset comprising cherry varieties from Turkey's Western Mediterranean region.