Crop leaf disease detection based on inter-layer feature distillation network
Aiming at the problem of insufficient utilization of limited labeled samples in existing crop leaf disease detection methods,which leads to low recognition accuracy and weak generalizability of the model,a crop leaf disease detection method based on inter-layer feature distillation network is proposed.The method adopts a meta-learning network structure with a support branch and a query branch supervising each other.Firstly,a set of shared weight feature extraction networks are used to map the input images of the two branches to the deep feature space,and multi-scale feature sets are constructed by using multiple down-sampling operations.Then,self-attention mechanism is calculated in each layer feature,and cross-attention mechanism is calculated between layers,aiming to enhance the robustness and reliability of feature expression at different scales and between scales.Finally,a knowledge distillation network is introduced in the cross-scale features,aiming to enrich the semantic information of low-level features with high-level features indirectly,and further enhance the robustness of feature expression at different scales and between scales.The proposed method has achieved recognition accuracies of 0.953 1,0.966 8,0.955 2 and 0.954 2 on potato,apple,tomato and corn diseases,respectively.
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