用人工智能驱动的叶病的检测,使用双融合-CBAM-静态用于产量保护和精准农业
Ruchika Bhuria1, Rahul Singh1, Mudassir Khan2,3
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Frontiers in plant science
|February 16, 2026
概括
这项研究引入了一种深度学习框架,用于自动分类南瓜叶病,达到96%的准确性. 这一进步有助于精准农业,因为它可以更快,更可靠地识别作物疾病.
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:
- 手动检查南瓜叶病是缓慢和主观的,阻碍了有效的精准农业.
- 自动检测对于及时管理疾病和优化作物产量至关重要.
研究的目的:
- 开发一个强大的深度学习框架,用于自动化南瓜叶病的分类.
- 提高在现实世界现场条件下疾病检测的准确性和效率.
主要方法:
- 提出了一个混合深度学习架构,DualFusion-CBAM-Stochastic,集成了DenseNet121和EfficientNetB3.
- 该模型结合了卷积块注意模块 (CBAM) 进行特征精细化和随机深度规范化,以提高概括性.
- 数据增强技术应用于5000种疾病类别的2000张南瓜叶图像数据集.
主要成果:
- 拟议的模型在南瓜叶病数据集上实现了96%的分类准确性.
- 与现有的基于卷积神经网络 (CNN) 的方法相比,废弃研究和比较分析显示出更高的性能.
- 该框架展示了增强的分类性能,特征可解释性和模型稳定性.
结论:
- 双脊柱融合,注意力机制和规范化技术的协同整合显著改善了自动疾病诊断.
- 这项研究为农业图像分析和自动作物疾病检测提供了坚实的基础.
- 开发的框架为早期检测南瓜叶病提供了可扩展和可靠的解决方案.
关键词:
库库比塔佩波叶病 (Cucurbita pepo foliar disease) 是一种植物的叶病.双融合-CBAM框架 双融合-CBAM框架计算式植物病理学深度学习架构的深度学习架构基于图像的疾病分类.精准农业 精准农业 精准农业智能作物健康监测 智能作物健康监测更多相关视频
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