使用混合深度学习和Grad-CAM可解释性的作物叶病的强大的多类分类
Sankar Murugesan1, Jayaprakash Chinnadurai2, Saravanan Srinivasan3
1Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India.
Scientific reports
|August 15, 2025
概括
一个新的深度学习模型有效地检测植物叶病,准确率为99.29%. 这种混合ConvNet-ViT模型的性能优于农业应用的现有方法.
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:
- 植物病严重影响作物产量和粮食安全.
- 准确和早期发现疾病对于有效的作物管理至关重要.
- 深度学习为自动植物疾病识别提供了有前途的解决方案.
研究的目的:
- 开发和评估一个有效的深度学习框架,用于检测和分类香,桃和番茄叶的疾病.
- 为了比较新型混合动力模型与先进的预训练模型的性能.
- 验证拟议模型在农业中的准确性和实际适用性.
主要方法:
- 利用公开可用的健康和病变植物叶子 (香,桃,西红) 的数据集.
- 预处理的数据用于深度学习架构,并分为培训,验证和测试集.
- 实施并将基线模型 (EfficientNetV2,ConvNeXt,Swin Transformer,ViT) 与一个新的混合ConvNet-ViT模型进行比较.
- 采用5倍交叉验证来提高分类器的性能并防止过拟合.
主要成果:
- 拟议的混合ConvNet-ViT模型实现了99.29%的测试准确性,超过了所有评估的预训练模型.
- 证明了结合卷积神经网络 (ConvNet) 局部特征提取与视觉转换器 (ViT) 全球上下文能力的有效性.
- 与单个最先进的模型相比,混合方法在分类植物叶病方面表现优越.
结论:
- 混合ConvNet-ViT模型是检测和分类植物叶病的高效和准确的解决方案.
- 该模型的卓越性能使其成为实际农业应用的有价值工具.
- 整合ConvNet和变压器框架可以增强基于图像的疾病检测能力.
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