EffResViT-SE FusionNet:一种混合深度学习框架,用于准确分类咖啡叶疾病
Bhoomika Mehta1, Salil Bharany1, Dalia H Elkamchouchi2
1Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India.
Food science & nutrition
|December 12, 2025
概括
一个新的深度学习模型,EffResViT-SE FusionNet,准确地检测咖啡叶疾病. 这种人工智能工具有助于农民早期识别疾病,提高作物产量和可持续农业实践.
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
- 人工智能的人工智能
背景情况:
- 咖啡种植面临着来自叶子疾病的重大威胁,如叶子生,Phoma,Cercospora和叶子矿工.
- 现有的诊断方法 (视觉检查,PCR,ELISA) 通常是缓慢的,昂贵的,需要专家知识,阻碍了广泛应用.
- 这些疾病严重影响咖啡植物的健康,降低光合作用效率,导致脱叶,降低作物产量和质量.
研究的目的:
- 开发一种新的自动化系统,用于早期和准确地检测和分类咖啡叶疾病.
- 通过利用先进的深度学习方法来解决传统诊断技术的局限性.
- 为咖啡种植中疾病管理提供可扩展和精确的解决方案.
主要方法:
- 提出了EffResViT-SE FusionNet,这是一个混合深度学习框架,将EfficientNetB3和ResNet50与Squeeze-and-Excitation (SE) 块和视觉转换器 (ViT) 结合起来.
- 在5个类别的58,555张咖啡叶图像的大数据集上训练模型:健康,矿工,叶,Cercospora和Phoma.
- 使用Adam优化器,学习率为0.001,批量大小为32,80个训练时代用于模型融合.
主要成果:
- 在检测咖啡叶病时,获得了99%的卓越的整体分类准确度.
- 在所有类别中表现出高性能,精度,回忆和F1分数从98%到99%不等.
- 显著优于基线模型 (ResNet50,EfficientNetB3,ViT) 的性能,精度提高高达5%.
结论:
- EffResViT-SE FusionNet为识别咖啡叶病提供了一个强大,精确和可扩展的解决方案.
- 混合架构有效地整合了本地和全球特征提取,以获得卓越的诊断准确性.
- 这种人工智能驱动的方法支持及时的疾病管理,有助于可持续的咖啡农业和农民生计.
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