PMVT:一种轻量级的视觉转换器,用于移动设备上的植物疾病识别
Guoqiang Li1, Yuchao Wang2,3, Qing Zhao1
1Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, Henan, China.
Frontiers in plant science
|October 12, 2023
概括
一个新的深度学习模型,基于植物的MobileViT (PMVT),为移动设备提供高效和准确的实时植物疾病检测. 这种轻量级架构在小麦,咖啡和大米数据集上实现了高精度,超过了现有模型的性能.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 农业计算资源有限,植物疾病多样化,使得精确,轻量级的疾病检测具有挑战性.
- 现有的深度学习模型经常难以平衡移动部署的准确性和计算效率.
研究的目的:
- 提出一个计算高效的深度学习架构,基于植物的MobileViT (PMVT),用于实时检测植物疾病.
- 设计一种适用于资源有限的移动设备的轻量级但高度准确的模型.
主要方法:
- 通过修改MobileViT开发了PMVT,使用7x7卷积内核将卷积块替换为倒置的残余结构.
- 在ViT编码器中集成了一个卷积块注意模块 (CBAM),以专注于基本功能并减少无关紧要的信息.
- 在各种农业数据集 (小麦,咖啡,大米) 上培训和评估PMVT,并开发了一个移动应用程序.
主要成果:
- 在数据集中,PMVT实现了高精度:小麦93.6% (0.98M参数),咖啡85.4%,大米93.1%.
- 与 MobileNetV3 和 SqueezeNet.Net.等既有轻量级模型相比,表现出卓越的性能.
- 该模型保持了高精度,参数数量减少,适合移动部署.
结论:
- 在移动设备上,PMVT为实时检测植物疾病提供了计算效率高,准确的解决方案.
- 该模型的架构有效地捕捉了长距离的依赖关系,并专注于关键特征,优于现有方法.
- 在植物疾病诊断应用程序中成功部署验证了其在各种场景中的实际实用性.
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