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BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8
Merve Varol Arısoy1, İlhan Uysal2
1Bucak Faculty of Computer and Informatics, Information Systems Engineering Department, Burdur Mehmet Akif Ersoy University, Burdur, Turkey. mvarisoy@mehmetakif.edu.tr.
Scientific Reports
|February 13, 2025
Summary
This study introduces a new deep learning model for accurate cherry variety classification. The hybrid model achieves over 91% accuracy, aiding agricultural practices and trade.
Area of Science:
- Agricultural Science
- Computer Science
- Machine Learning
Background:
- Accurate cherry variety classification is vital for economic value and market differentiation.
- Genetic diversity and visual similarity of cherries pose challenges for manual identification.
- Inefficient identification practices hinder agricultural and trade operations.
Purpose of the Study:
- To develop a novel deep learning-based hybrid model for accurate cherry variety classification.
- To address the limitations of manual identification in agricultural and trade contexts.
- To enhance efficiency in harvest timing, quality control, and export classification.
Main Methods:
- A hybrid deep learning model integrating BiFPN with YOLOv8n-cls framework.
- Enhancement of the model using Swin Transformer and Deformable Attention Transformer (DAT) techniques.
- Training and evaluation on a newly constructed dataset of Turkish cherry varieties.
Main Results:
- The proposed model achieved high performance metrics.
- Precision: 91.91%, Recall: 92.0%, F1-score: 91.93%, Overall Accuracy: 91.714%.
- Demonstrated the effectiveness of the hybrid deep learning approach.
Conclusions:
- The developed model offers a robust solution for automated cherry variety classification.
- Findings support optimization of harvest timing, quality control, and export classification.
- Contributes to improved agricultural practices and economic outcomes in the cherry industry.
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