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根据CBAM-MobileNetV2和转移学习的基础上对类黄龙的分类

Shiqing Dou^1,2, Lin Wang^1,2, Donglin Fan^1,2

¹College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)

|July 8, 2023

概括

一个具有转移学习的新CBAM-MobileNetV2模型在图像中准确识别类黄龙病. 这种先进的模型显著改善了早期检测,帮助农民保护类作物,促进农业发展.

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:

类黄龙对中国南部的水果种植者构成重大挑战,影响农业发展和农民收入.
准确及时诊断类黄龙对于有效的疾病管理至关重要.

研究的目的:

开发一种新的分类模型,使用图像识别来诊断类黄龙.
通过先进的深度学习技术,提高类黄龙检测的准确性和效率.

主要方法:

开发了一个CBAM-MobileNetV2模型,将卷积特征与注意模块集成在一起,以增强信息捕获.
转移学习,特别是参数微调,用于优化模型性能.
增加了751张果图像的数据集,并将其分为训练 (80%) 和测试 (20%) 集.

主要成果:

该CBAM-MobileNetV2模型实现了高识别准确度为98.75%的类图像.
转移学习中的参数微调性能优于参数结,精度提高了1.02-13.6%.
与标准的MobileNetV2,Xception和InceptionV3模型相比,开发的模型表现出卓越的性能.

结论:

结合转移学习的CBAM-MobileNetV2模型提供了一个非常准确的图像识别解决方案,用于花 huanglongbing.
这种方法为早期疾病诊断提供了一个有前途的工具,支持可持续的果种植实践.

关键词:

这就是为什么CBAM是CBAM.移动网络V2 移动网络V2 果花黄龙平黄龙平疾病分类疾病分类.图像识别功能图像识别功能转移学习转移学习

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