Abstract: In this paper, a study is conducted on corn haploid seeds recognition based on convolutional neural network using 3000 corn seed images with 1230 haploid corn seed images and 1770 diploid corn seed images. In order to compare the effect of different convolutional neural network models on haploid corn seeds recognition, classical models including VGG, ResNet, DenseNet and SKNet are adopted, and the SKNet model is improved by replacing the fully-connected layer in dimensionality reduction and dimensionality increase with one-dimensional convolution to further reduce the number of model parameters, and the improved SKNet is called ECA_SKNet. The experimental results show that aforementioned five models can achieve good recognition of haploid corn seeds with the lowest accuracy of 88.5% and the accuracy of ECA_SKNet can reach 93.04%. It is seen that convolutional neural networks can play an important role for the recognition of corn haploid seeds and provide a new way to recognize crop seeds.