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Aim to identify and position corn seed, an object detection model based on improved YOLO v4 was proposed. This model combined with multi-spectral images with four channels (RGB+NIR), the appearance quality of corn seeds was identified and classified. In order to reduce the number of parameters in the model, the trunk feature extraction network was replaced with the lightweight network MobileNet V1. To improve the performance of the model, the effect of spatial pyramid pooling (SPP) structure on the model performance was studied. Finally, the improved YOLO v4-MobileNet V1 model was selected to detect the appearance quality of corn seeds. The experimental results showed that the comprehensive evaluation indexes F1 and mAP of the model reached 93.09% and 98.02%, respectively. The average detection time of each image was 1.85s, and the average detection time of each corn seed was 0.088s. And the number of model parameters was compressed to 20% of the original model. The spectral band of four channel multi-spectral image can be extended beyond the visible range. Image can extract more representative feature information. The improved model had the advantages of strong robustness, good real-time performance and lightweight. It can provide a reference for high throughput quality detection and optimal classification of seeds.