To address the low efficiency and accuracy of manual observation in recognition of crop development stages,a recognition approach based on I_CBAM-DenseNet model is proposed.The approach utilizes a densely connected convolutional network (DenseNet) as the backbone extraction network and incorporates a Convolutional Block Attention Module (CBAM).The Spatial Attention Module (SAM) and Channel Attention Module (CAM) in CBAM are modified from traditional serial connection to parallel connection,and the Improved CBAM (I_CBAM) is inserted into the last dense block of DenseNet to construct the I_CBAM-DenseNet model.Seven important development periods of wheat are selected for automatic identification.To maximize wheat feature extraction,the Excess Green (ExG) feature factor and the maximum inter-class variance method of Otsu are combined to segment the acquired wheat images.The accuracy and loss values of models including I_CBAM-DenseNet,AlexNet,ResNet,DenseNet,CBAM-DenseNet and VGG are compared and analyzed.The results show that the proposed I_CBAM-DenseNet model outperforms other models with a high accuracy of 99.64%.