This paper proposed a method for detecting apple at night based on YOLOv8n with fusion of image enhancement and transfer learning to address the issues of low recognition rate and poor real-time performance of apples in nighttime scenarios. Firstly， embedding a Zero-DCE module in the front-end of YOLOv8n enhanced images of apple at night， presented the contours and details of apples more clearly， and reduced the difficulty of recognizing images of apple at night. Secondly， using SPD-Conv for down-sampling enhanced the ability of the model to extract fine-grained features. On this basis， transfer learning training strategy was used to solve the problem of small sample size in the dataset of apple at night. The MS COCO dataset containing categories of apple was selected as the source domain dataset. In term of the target domain dataset in nighttime scenarios， Zero-DCE was used to increase its similarity with images of apple during the day and finely tune the model of target domain on the model of source domain. Experiments were conducted on the image dataset of apple at night based on the method above. The results showed that the model accuracy P， a recall R， and an average accuracy mean mAP@0.5：0.95 of method proposed was 97.0%，93.4% and 74.6%， being 2.3，1.9， and 4.3 percentages higher than that of the YOLOv8n original model. The inference speed of this model was 22 frames/s， meeting requirements of real-time detection. The results of the ablation experiment showed that the combined effect of image enhancement and transfer learning exceeded the sum of the effects when applied separately. The improved model performed better than the original model in dealing with complex situations including overlap， occlusion， green fruits， and dim lighting， and had good robustness.