基于EAMnet的小麦开花期品种识别研究OA

To solve the problems of low efficiency,low accuracy,and insufficient related research in traditional recognition methods,a wheat flowering period variety recognition model based on improved Resnet34 is proposed.Firstly,to address the problem that existing agricultural recognition models have a large number of parameters that are not conducive to deployment on mobile devices,an improved Inceptionv1 mod-ule is used to replace the second convolutional block of the basic residual block of the Resnet34 network,reducing the model parameter count by about half;Secondly,in response to the problem of decreased recognition accuracy after the reduction of model parameters,ECA and si-mAM attention mechanisms are added to the model to improve the accuracy of wheat flowering stage variety recognition through effective extrac-tion of wheat features.The experimental results show that the proposed model has an average recognition accuracy of 95.7%on the wheat flow-ering stage dataset,which is 2.1%higher than the original Resnet34 model.Compared with the efficientnetv2_s,MobileNet-v2,and GoogLeNet models,the accuracy has been improved by 2.4%,3.2%,and 5.0%,respectively.The proposed model has better feature extrac-tion ability and provides an effective method for identifying wheat varieties during the flowering period.