Aiming at the problems of low precision and slow detection of daylily maturity, an intelligent detection method based on YOLOv8 daylily maturity is proposed. This intelligent detection method incorporates the SimAM into the C2f module to enhance the model's attention towards daylily. The utilization of the CARAFE module aims to enhance the extraction of crucial features. Furthermore, the study employs the Inner-loU as a loss function to enhance the network's bounding box regression performance. The findings from the experiment indicate that the YOLOv8 Daily algorithm enhances both detection precision and recall by 1.55% and 1.00%, respectively, in comparison to the baseline model. The mAP@0.5 achieved by the YOLOv8 Daily algorithm is 88.28%, and the average reconciliation value is enhanced to 0.86. Furthermore, the algorithm demonstrates a real-time detection speed of 283.2 frames per second. These results offer valuable insights for further exploration in the field of intelligent and real-time detection of daylily.