一个双轨特征融合模型,使用组混动残留变形网和swin变压器进行葡萄叶病的分类
R Karthik1, Gadige Vishnu Vardhan2, Shreyansh Khaitan2
1Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, India. r.karthik@vit.ac.in.
Scientific reports
|June 24, 2024
概括
这项研究引入了一种用于葡萄叶病检测的自动深度学习模型. 这种新型的双轨网络实现了高精度,提高了葡萄种植中的疾病分类效率.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 葡萄种植在经济上具有重要意义,但受到疾病威胁.
- 手动识别疾病是劳动密集型的,需要专业知识.
- 开发自动检测方法对于有效的疾病管理至关重要.
研究的目的:
- 为葡萄叶病检测设计一种自动化的深度学习方法.
- 开发一种新的双轨网络,用于准确分类葡萄叶病.
- 提高葡萄疾病识别的效率和可扩展性.
主要方法:
- 开发了一个双轨网络,结合了Swin变压器和Group Shuffle剩余变形网 (GSRDN).
- 斯温变压器使用移动窗口提取全球特征.
- 通过使用专用块,GSRDN有效地提取本地特征,然后进行三重注意力集成.
主要成果:
- 该模型在葡萄叶疾病分类中实现了98.6%的准确性.
- 记录了高精度 (98.7%),回忆 (98.59%) 和F1得分 (98.64%).
- 对PlantVillage数据集的验证证实了该模型的有效性.
结论:
- 拟议的深度学习模型为葡萄叶病的检测提供了高效和准确的解决方案.
- 这种自动化方法可以显著帮助疾病管理,提高葡萄的产量和质量.
- 新型网络架构显示出在葡萄栽培中实际应用的潜力.
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