Leaf disease detection based on improved U-Net network model
To meet the demand of green prevention and control of crop diseases and pests for the detection of disease and pest severity,an improved U-Net network model is designed for the detection of crop leaf disease and pest severity.First,the ResNet50 network is selected as the backbone network of the model,and transfer learning is used to improve the training convergence speed and reduce the computational cost.Second,the attention mechanism is introduced to optimize the feature extraction and fusion of each layer of the U-Net network,so as to improve the ability of the network model to receive key information.The experimental results show that the improved U-Net512 network model has the best detection performance,with an average detection accuracy of 90.14%and an average absolute error of 276.3.By analyzing the feature maps of each layer of the model under different sampling depths,it is found that the introduction of attention mechanism enables the network model to obtain and fuse two dimensions of information:the overall feature of the leaf and the disease area feature,which further improves the model detection performance.This method can not only effectively detect the disease and pest severity of crop leaves,but also has high accuracy and reliability,which is conducive to achieving green prevention and control of crop diseases and pests.
pest detectionU-Net networkattention gatepest control
国家科技期刊平台
NSTL
万方数据
