Abstract: To address the problems of low efficiency of traditional manual identification and inconsistent identification standards, a ripening identification method for Hemerocallis citrina baroni based on lightweight and efficient layer aggregation network LSEB YOLO v7 was proposed. Firstly, lightweight convolution was introduced to lighten the efficient layer aggregation network and transition module to reduce the model computation. Secondly, the channel attention mechanism was added between the feature extraction and feature fusion networks to improve the model detection performance. Finally, in the feature fusion network, the channel information fusion method was optimized, and the bi-directional feature pyramid network was used to replace concatenate to increase the information fusion channels and continuously improve the model performance. The experimental results showed that compared with the original algorithm, in the Hemerocallis citrina baroni maturity detection, the number of parameters and floating-point operations of the improved LSEB YOLO v7 algorithm were reduced by about 2.0×10~6 and 7.7×10~9, respectively, and the training time was reduced from 8.025 h to 7.746 h, and the model volume was compressed by about 4 MB. Meanwhile, the training precision and recall were improved by about 0.64 percentage and 0.14 percentage, respectively. The mAP@0.5 and mAP@0.5:0.95 were improved by about 1.84 percentages and 1.02 percentages, respectively. In addition, the harmonized mean remained unchanged at 84.00%. It was evident that the proposed LSEB YOLO v7 algorithm solved the problem of the paradox between model complexity and performance, and provided technical support for intelligent ripening and harvesting inspection equipment for Hemerocallis citrina baroni.