Abstract: In order to realize the fast and accurate optimization of corn seeds, a watershed algorithm combined with convolutional neural network was proposed to detect the quality of corn seeds with different quality as the research object. Firstly, the watershed algorithm was used to divide the single corn seed, and then the quality of each seed was classified by the convolutional neural network model. According to the position of the single seed obtained by the watershed algorithm, the results were labeled in the image to realize the quality detection of seeds. The improved InceptionV3 model was used to test the seeds. The test results showed that the average accuracy rate, the average recall rate and the F1 value（harmonic average evaluation） of the two kinds of seeds with good quality and defects were 94.18%, 94.61% and 94.39%. Meanwhile, in order to highlight the performance of the convolutional neural network model, the results were compared with the traditional machine learning method, and the F1 value of the convolutional neural network model was 20.39% higher than that of the LBP+SVM model.