Potato disease detection based on improved UNet semantic segmentation model
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.
NETL
NSTL
万方数据
