To achieve intelligent citrus picking, fast and accurate identification of citrus in the orchard environment becomes critical. Aiming at the defective adaptation of existing target detection algorithms to the environment and low efficiency, this study proposes a lightweight citrus maturity detection algorithm based on the YOLOv8n model, YOLOv8n-CMD (YOLOv8n citrus maturity detection). Firstly, the backbone network structure is optimized to improve the detection of small targets. Secondly, the CBAM attention mechanism is added to improve the classification effect of the model. Then, Ghost convolution is introduced, and the neck C2f module in the original YOLOv8 model is combined with Ghost to reduce the amount of computation and that of parameters. Finally, the SimSPPF module is used in place of the original pyramidal pooling layer to improve model detection efficiency. Experimental results show that the YOLOv8n-CMD algorithm reduces the number of parameters and computation by 31.8% and 7.4%, respectively, and improves the accuracy by 3.0%, which is more suitable for citrus detection research in the orchard environment.