•Dilated convolution kernel enlarges local receptive field and enhances feature extraction.•Global pooling layer reduces training parameters number and avoids overfitting problem.•Multi-scale convolutional kernels extract multi-scale features of the input image.•Improvement of recognition accuracy and robustness is verified by the experimental results. It is a challenging research topic to identify plant disease based on diseased leaf image processing techniques due to the complexity of the diseased leaf images. Deep learning models are promising for identifying plant disease based on leaf images and AlexNet is one of these models. Aiming at the problems of too many parameters of the AlexNet model and single feature scale, a global pooling dilated convolutional neural network (GPDCNN) is proposed in this paper for plant disease identification by combining dilated convolution with global pooling. Compared with the classical convolutional neural network (CNN) and AlexNet models, GPDCNN has three improvements: (1) the convolution receptive field are increased without increasing the computational complexity and without losing the discriminant formation by replacing fully connected layers with a global pooling layer; (2) dilated convolutional layer is employed to recover the spatial resolution without increasing the number of training parameters; (3) GPDCNN also integrates the merits of dilated convolution and global pooling. Experimental results on the datasets of six common cucumber leaf diseases demonstrate that the proposed model can effectively recognize cucumber diseases.