###### Abstract
In the literature [10], the authors compressed the model based on the YOLOv4 algorithm in order to realize the intelligent planting and management of figs, simplified the model structure, and realized fig detection in complex scenes, but the training process is redundant, and the model structure is still able to be further optimized. A multisensory deep learning approach was proposed in the literature [11] to achieve the detection and counting of cotton bolls. The authors increased the detail extraction of boll features by introducing a multiscale residual module and an attention mechanism, thus improving the detection precision. An average precision [EMAIL_ADDRESS] of 92.75% was finally achieved. Different from the current study, in the literature [12], the authors analyze the external environmental conditions that can affect the transmission of visual data during the construction of smart agriculture and remote monitoring of the growth of fruits and vegetables by comprehensively considering factors such as species classification, plant diseases [13], yield, weather and soil [14], etc., and realized the recognition and detection of different parts of cabbage using convolutional neural networks and image processing technology. The detection precision reached 97%, but the advantage of the model's lightweight is not obvious. In the literature [15], the authors used the YOLO algorithm to automatically identify the stomata of corn leaves, which compensated for the shortcomings of manual observation, improved the detection efficiency, reduced the false detection rate, and saved the observation time, but the loss function convergence of the proposed model was unsatisfactory. The growth characteristics of Camellia sinensis are similar to those of Hemerocallis citrina Baroni; the plants are dense and need to be picked selectively at regular intervals. Therefore, in the literature [16], a highly integrated and lightweight deep learning model is proposed using multiple residual networks as the main framework, which is capable of efficiently detecting chamomile, and, finally, an average precision of 92.49% was achieved. Most importantly, computer vision and target detection are widely used in the field of yellow flowers. In the literature [17], based on the YOLOv4 algorithm, the authors designed the dense-YOLOv4 network structure with the K-means++ clustering algorithm to address the shortcomings of the algorithm in terms of precision and speed for the detection of Hemerocallis citrina Baroni. Similarly, in the literature [18], for Hemerocallis Citrina Baroni maturity detection, a lightweight network model was constructed in the YOLOv7 target detection algorithm using the MobileOne network as the backbone feature extraction network and incorporating the coordinate mechanism in the neck network to improve the detection of samples. This method has low detection precision for complex backgrounds. In the literature [19], the authors used algorithms to detect Hemerocallis Citrina Baroni diseases, improving the ability to control them in the early stages of development. The development of convolutional neural networks has gradually matured, and their performance has been steadily improving, allowing for real-time detection with high precision in multiple fields and scenarios. However, the model structure is becoming more and more complex, and the neural network is gradually developing in the direction of large models and deep networks. Therefore, in Hemerocallis citrina Baroni maturity detection, for the problems of multi-branching, large computation, and low efficiency of the YOLOv7 [20] model, this paper proposes the GCB (Ghost-CBAM-Bi directional feature pyramid network) YOLOv7 algorithm. The main innovations are as follows:  
1.