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实时杂草识别与移动设备的增强的mobilevit模型

Xiaoyan Liu¹, Qingru Sui², Zhihui Chen¹

¹Changchun Sci-Tech University, Changchun, 130600, China.

Scientific reports

|July 27, 2025

概括

本研究介绍了一种轻量级的深度学习模型,用于在移动设备上高效地识别杂草. 增强的MobileViT架构实现了高精度和实时性能,优化了农业技术.

科学领域:

农业技术农业技术
计算机视觉计算机视觉计算机视觉
深度学习是一种深度学习.

背景情况:

深度学习模型显著提高了杂草识别的准确性.
现有的模型往往太大,对于移动嵌入式系统来说太慢.
对于移动杂草识别的优化研究是有限的.

研究的目的:

为移动嵌入式系统开发一种轻量级的杂草识别模型.
为了在实际应用中平衡高精度与实时性能.
解决大型,缓慢的卷积神经网络 (CNN) 的局限性.

主要方法:

提出了一个增强的MobileViT架构用于杂草识别.
整合了高效通道注意 (ECA) 模块用于特征提取.
使用多级视网膜增强算法与颜色恢复用于图像预处理.

主要成果:

在杂草识别方面获得了98.51%的F1分数.
显示平均识别时间为每张图像89毫秒.
该模型有效地平衡了高精度与低计算复杂性的高精度.

结论:

拟议的轻量级MobileViT模型适合在移动设备上实时识别杂草.
该模型通过将复杂性降至最低,并最大限度地提高性能,为精密农业提供了实用解决方案.
这项研究有助于在农业中推进高效的人工智能.

关键词:

在美国,CNN是CNN.嵌入式设备嵌入式设备移动ViT 移动ViT 在线模型优化模型优化杂草的识别杂草的识别

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