BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8

Abstract

Abstract 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. Experimental results demonstrated the effectiveness of the proposed approach, achieving a precision of 91.91%, recall of 92.0%, F1-score of 91.93%, and an overall accuracy of 91.714%. The findings highlight the model’s potential to optimize harvest timing, ensure quality control, and support export classification, thereby contributing to improved agricultural practices and economic outcomes.

Keywords

• Cherry classification
• Multiple attention YoloV8
• Swin transformer
• DAT