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Abstract: To address the problems of low recognition accuracy, large number of model parameters, slow inference speed and difficult deployment of the current convolutional neural network seed sorting method, a seed sorting method based on lightweight pyramidal dilated convolutional network is proposed. This paper proposes the residual spatial pyramid module, which expands the perceptual field by using the convolution of dilated with different expansion rates, so as to effectively extract multi-scale features. Deeply separable convolution techniques are then used to reduce the number of model parameters and the computational complexity. A lightweight attention mechanism module is introduced into the network structure to improve seed key feature extraction by focusing on important information using local cross-channel interactions. The experimental results show that the proposed network has only 0.13M parametric number, 96.00% and 97.38% accuracy on corn dataset and red kidney bean dataset, and 4.51ms average time to recognize a single image on NVIDIA Quadro boards, which are better than the mainstream lightweight networks MobileNetv2, Shufflenetv2 and PPLC- Net, etc., which can meet the requirements of real-time recognition in industrial sites.