一个具有注意力机制的多核CNN模型用于对类植物疾病的分类
Shiny R M1, Angelin Gladston2, Khanna Nehemiah H3
1Department of CSE, St. Joseph's College of Engineering, OMR, Chennai, India. shinyrm@gmail.com.
Scientific reports
|July 5, 2025
概括
本研究介绍了一种多核卷积神经网络 (CNN),用于早期植物疾病分类的注意力机制. 该模型准确识别疾病,有助于农业生产和经济稳定.
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:
- 植物疾病显著降低农业产量,影响经济.
- 早期发现和分类叶疾病对于维持生产和经济增长至关重要.
研究的目的:
- 提出一个具有注意力机制的多核CNN模型,用于准确的类植物疾病分类.
- 加强早期识别叶疾病,以改善水果生产.
主要方法:
- 图像预处理,包括从不同的数据集进行大小调整.
- 使用预训练的卷积神经网络提取特征.
- 应用多核通道注意力和空间注意力机制来生成特征地图.
- 将图像分为正常和疾病类别的分类.
主要成果:
- 拟议的多核CNN模型与注意力机制实现了高分类性能.
- 该模型在测试准确性方面表现出优越的性能,与其他现有模型相比.
结论:
- 开发的模型有效地对类植物疾病进行了分类,为早期检测提供了有前途的工具.
- 通过这种模型准确识别疾病,可以有助于提高果产量和经济效益.
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