用BiFPN增强的基于SwinDAT的桃品种分类与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
概括
这项研究引入了一个新的深度学习模型,用于准确的桃品种分类. 混合模型的准确度超过91%,有助于农业实践和贸易.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 精确的桃品种分类对于经济价值和市场差异化至关重要.
- 桃的遗传多样性和视觉相似性对手工识别提出了挑战.
- 不有效的识别实践阻碍了农业和贸易活动.
研究的目的:
- 开发一种基于深度学习的新型混合模型,用于准确的桃品种分类.
- 解决农业和贸易环境中手动识别的局限性.
- 提高收获时间,质量控制和出口分类的效率.
主要方法:
- 一个混合深度学习模型,将BiFPN与YOLOv8n-cls框架集成在一起.
- 使用Swin变压器和可变形注意力变压器 (DAT) 技术增强模型.
- 在新建的土耳其桃品种数据集上进行培训和评估.
主要成果:
- 拟议的模型实现了高性能指标.
- 精度:91.91%,回忆:92.0%,F1得分:91.93%,整体准确率:91.714%,回忆:92.0%,F1得分:91.93%,整体准确率:91.714%.
- 证明了混合深度学习方法的有效性.
结论:
- 开发的模型为自动化桃品种分类提供了强大的解决方案.
- 研究结果支持优化收获时间,质量控制和出口分类.
- 有助于改善桃行业的农业实践和经济成果.
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