DB-DAB-Net:一种用于玉米叶片病害识别的高效新型网络OA

Maize is a vital global food crop,yet its production is vulnerable to various diseases.Under complex backgrounds,the existing deep learning models often suffer from insufficient multi-scale feature extraction and inefficient fusion of contextual information,leading to low accuracy in disease identification.To address these issues,this paper proposes a maize leaf disease recognition model(DB-DAB-Net),which combines a two-branch feature extraction structure with the attention mechanism.Firstly,Depthwise Separable Conv is constructed as a two-branch feature extraction structure to capture complex features and spatial details while reducing computational cost and parameter count.A dynamic fusion gating mechanism is introduced to adaptively integrate the double branch,enhancing the feature extraction efficiency and model robustness.Then,MCSA module is incorporated after each stage,which combines channel attention and spatial attention.A bidirectional interactive weight mechanism is introduced to achieve fine capture of global and local information while minimizing calculation cost.Finally,BiFPN module is added in front of the average pooling layer,which performs multi-scale feature fusion through the bidirectional feature pyramid network,dynamically adjusts features at different scales using learnable weights,and employs Swish activation function to ensure gradient stability,so as to further improve the model's perception ability of multi-scale targets.The experimental results show that DB-DAB-Net achieves good identification performance on maize disease data set,with the identification accuracy rate,recall rate,F1 score and accuracy rate reaching 97.58%、97.47%、97.49%and 97.47%,respectively.The number of parameters and floating-point operations of the model are 2.53 M and 5.56 G,respectively.In complex environment,DB-DAB-Net model can effectively improve the accuracy of maize leaf disease detection,providing a new technical idea for agricultural disease monitoring.