Plant disease recognition method based on lightweight model with attention mechanism
In light of the issues associated with slow response speed,numerous parameters,and high computational memory requirements in existing plant disease recognition models,we proposed a lightweight neural network model.The model consisted of feature extraction layer,feature enhancement layer,and classifier.To reduce model size and increase network re-sponse speed,we utilized deep separable convolution in the feature extraction layer.To prevent gradient disappearance during network propagation and enhance the fusion of disease pixel features,we introduced the inverted residual block convolution kernel structure(IRBCKS)module into the feature extraction layer.Furthermore,we integrated a lightweight convolutional block attention module(CBAM)attention mechanism into the feature enhancement layer to capture the relationships between pixels in plant disease-related images and enhance key information extraction.Finally,we employed a pruning technique to eliminate redundant feature information from the base model,thereby reducing the number of model pa-rameters once again,yielding this lightweight network mod-el,Cut-MobileNet.In order to verify the progressiveness of this model,it was compared with lightweight models(MobileNet V2,SqueezeNet,GoogLeNet)and non-lightweigh models(Vision Transformer,AlexNet).The results show that better results have been achieved by Cut-MobileNet in floating-point operation,accuracy,single image inference time,parameter count,F1 value,and model size.
万方数据
维普
