通过ConvNeXt和视觉变压器融合,提高注意力的混合深度学习模型用于强大的果叶疾病分类
1Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Rize, Türkiye.
Frontiers in plant science
|August 18, 2025
概括
这项研究介绍了MangoLeafCMDF-FAMNet,这是一种用于识别果叶病的深度学习模型. 它在疾病分类方面取得了很高的准确性,有助于可持续农业.
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:
- 果种植在经济上具有重要意义,需要有效的疾病管理.
- 准确识别果叶病对作物健康和生产率至关重要.
研究的目的:
- 开发一个先进的深度学习框架,用于对果叶病的多类分类.
- 提高果植物自动疾病诊断的准确性和稳定性.
主要方法:
- 提出了CMDF-FAMNet的MangoLeaf,这是一个混合深度学习模型,结合了ConvNeXt和Vision Transformer.
- 集成了一个特征注意模块 (FAM) 来改善特征歧视.
- 为了强大的功能嵌入,利用了一种新的跨模式动态融合策略.
主要成果:
- 在三个基准数据集上实现了0.9978,0.9988和0.9943的特殊分类准确性 (CA).
- 在现有的基线模型中表现出优越的性能.
- 获得了强大的马修斯相关系数 (MCC) 和科恩的卡帕分数.
结论:
- 果叶CMDF-FAMNet是高度有效和可通用的自动果叶疾病诊断.
- 该框架推进了精密植物病理学的深度学习应用.
- 通过改善疾病管理,为可持续的农业实践做出贡献.
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