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使用人工智能诊断类绿化:一个更快的基于区域的卷积神经网络方法与卷积块注意模块集成的VGGNet和ResNet模型

Ruihao Dong¹, Aya Shiraiwa², Achara Pawasut³

¹Faculty of Informatics, Kansai University, Takatsuki 569-1095, Osaka, Japan.

Plants (Basel, Switzerland)

|June 27, 2024

概括

在树木中检测类绿化 (CG) 疾病对于管理至关重要. 深度学习模型,特别是与CBAM配合的ResNet,在从图像中识别CG方面表现出高效率,从而实现实时农民诊断.

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:

绿化 (CG) 或黄龙是一种由载体传播的破坏性类疾病.
目前的管理依赖于集成策略,如载体控制和移除受感染的树木.
有效检测CG感染的树木对于有效的疾病管理至关重要.

研究的目的:

评估深度学习模型,以有效地检测果绿化.
为了比较更快的R-CNN,VGGNet和ResNet架构的性能.
评估卷积块注意模块 (CBAM) 对检测准确性的影响.

主要方法:

使用转移学习开发了更快的R-CNN模型.
进行预训练的VGGNet和ResNet模型的比较.
集成的卷积区注意模块 (CBAM) 与VGGNet和ResNet一起创建VGGNet+CBAM和ResNet+CBAM的变体.
在网页应用程序中部署了高效的模型,以提高农民的可访问性.

主要成果:

基于ResNet的模型在CG检测方面表现出卓越的性能.
集成CBAM显著提高了检测精度和整体模型性能.
网络应用程序使CG疾病的实时现场诊断成为可能.

结论:

深度学习,特别是ResNet+CBAM,为绿化检测提供了高效的解决方案.
开发的网络应用程序为农民提供了一个实用的工具,可以实时诊断CG.
改进的检测有助于及时干预和更好地管理这种破坏性类病.

关键词:

美国有线电视新闻 (CNN) 模特儿注意力机制注意力机制深度学习是一种深度学习.对象检测检测对象检测对象检测植物疾病检测检测检测检测转移学习转移学习

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