Crop Pest Identification Method Based on Improved ThreshNet Model
Aiming at the problems of poor feature extraction ability and low generalization performance of existing deep learning models for pests in complex backgrounds,a crop pest recognition method based on improved ThreshNet model is proposed.Firstly,a bulk channel normalization module(BCN)is introduced to improve the generalization ability of the model;secondly,a high level screening feature pyramid network(HSFPN)is fused with a customized convolution module(partial-conv)to form a multi-scale feature fusion module(PCHS).Secondly,the module is embedded into the model between the dense connections and the harmonic dense connections to enhance the model's feature extraction capability.Finally,the overall architecture of the model is adjusted to obtain the improved TNP model.Comparison experiments are carried out on the self-built dataset P28.The experimental results show that compared with the pre-improvement model,the accuracy of the TNP model increases by 3.95%,the number of parameters decreases by 6.47M,and the amount of floating-point operations(FLOPs)decreases by 0.23G.Compared withResNet50,DenseNet,EfficientNet B4 and other models,the TNP model has better performance in terms of accuracy,parameter quantity,FLOPs and inference time.The improved model can quickly and accurately identify the characteristic information of crop pests,and provide technical support for timely prevention and control of pests.
万方数据
维普
