基于改进型UNet语义分割模型的马铃薯病害检测方法  

Potato disease detection based on improved UNet semantic segmentation model

作　　者：李旭东 王林柏 张博[1] 孙磊[1] 刘景艳[1] 范晓飞 LI Xudong;WANG Linbai;ZHANG Bo;SUN Lei;LIU Jingyan;FAN Xiaofei(College of Mechanical and Electrical Engineering,Hebei Agricultural University,Baoding 071001,China)

机构地区：[1]河北农业大学机电工程学院,河北保定071000

出　　处：《河北农业大学学报》2024年第6期121-126,共6页Journal of Hebei Agricultural University

基　　金：国家自然科学基金项目(32070572);国家现代农业产业技术体系项目(CARS-23).

摘　　要：深度学习技术和卷积神经网络的发展,为农作物病害快速准确检测提供了新的解决方法。本文实地采集马铃薯图像,使用UNet语义分割模型对马铃薯病害进行检测,使用了2种主干网络模型VGG16和ResNet50,它们的精确率为93.00%,F1为92.48%和92.77%,MPA为94.47%和94.42%,MIoU为84.79%和84.75%。提出了一种改进型UNet语义分割模型,通过在网络的第一次上采样处加入注意力机制模块获取特征图,将注意力机制获得的特征图乘以原输入的特征图进行下一步的上采样网络过程,最终取得的Precision、F1、MPA和MIoU分别为94.83%、92.89%、95.96%和86.32%。相较于初始网络的指标有所提高,为自然环境下马铃薯叶片病害识别和检测提供较为全面的深度学习算法和模型研究基础。The development of deep learning technology and convolutional neural network has provided a new solution for the rapid and accurate detection of crop diseases.In this paper,potato images were collected in the field,and the UNet semantic segmentation model was used to detect potato diseases.Two backbone network models VGG16 and ResNet50 were used,whose precision was 93.00%,F1 was 92.48%and 92.77%,and MPA was 94.47%and 94.42%,MIoU was 84.79%and 84.75%.An improved UNet semantic segmentation model was proposed.The feature map was obtained by adding an attention mechanism module at the first upsampling of the network,and the feature map obtained by the attention mechanism was multiplied by the original input feature map for the next step.During the sampling network process,the final Precision,F1,MPA and MIoU were 94.83%,92.89%,95.96%and 85.32%,respectively.Compared with the initial network,the index was improved,which provided a more comprehensive deep learning algorithm and model research basis for the identification and detection of potato leaf diseases in natural environment.

关 键 词：马铃薯叶片病害 语义分割 注意力机制 卷积神经网络 

分 类 号：S24[农业科学—农业电气化与自动化]