Aiming at the problems of low efficiency of manual observation and low recognition accuracy of crop development stage in China, a wheat development stage recognition method based on I_CBAM-DenseNet model is proposed. The method takes a dense connected convolutional network (DenseNet) as the backbone extraction network and incorporates a convolutional attention module CBAM. firstly, the spatial attention module (SAM) and channel attention module (CAM) in CBAM are changed from the traditional serial connection to parallel connection, and the improved CBAM (I_CBAM) is inserted into the last dense network of DenseNet. An I_CBAM-DenseNet model is constructed, and then seven important development periods of wheat are selected for automatic identification. In order to maximize the extraction of the feature information of wheat, the ultra-green feature of ExG factor and the the maximum inter-class variance method are combined to segment the acquired wheat images. The changes in accuracy and loss values of the models I_CBAM-DenseNet, AlexNet, ResNet, DenseNet, CBAM-DenseNet and VGG are comparatively analysed. The results show that the model built by taking the convolutional neural network based on I_CBAM-DenseNet has an accuracy of 99.64%, which is higher than the comparison models.