BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8

Bibliographic Details
Title:	BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8
Authors:	Merve Varol Arısoy, İlhan Uysal
Source:	Scientific Reports, Vol 15, Iss 1, Pp 1-19 (2025)
Publisher Information:	Nature Portfolio, 2025.
Publication Year:	2025
Collection:	LCC:Medicine LCC:Science
Subject Terms:	Cherry classification, Multiple attention YoloV8, Swin transformer, DAT, Medicine, Science
More Details:	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.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2045-2322
Relation:	https://doaj.org/toc/2045-2322
DOI:	10.1038/s41598-025-89624-7
Access URL:	https://doaj.org/article/7570ffffa1c7429483cf2931579d7710
Accession Number:	edsdoj.7570ffffa1c7429483cf2931579d7710
Database:	Directory of Open Access Journals
Full text is not displayed to guests.	Login for full access.

More Details
ISSN:	20452322
DOI:	10.1038/s41598-025-89624-7
Published in:	Scientific Reports
Language:	English