## 3 Experimental Analysis
### Comparison of Test Results  
In the process of Hemerocallis citrina Baroni maturity detection, the same parameters were set for detection, in which the IOU threshold and confidence threshold were 0.2 and 0.2, respectively. Because NMS is added to both the YOLOv7 and GCB YOLOv7 algorithms, the non-maximal suppression effect is obvious. This reduces the shortage of duplicate detection to a greater extent and improves the local search and comparison abilities of the model [28]. Influenced by the growth and harvesting characteristics of Hemerocallis citrina Baroni, its harvesting is mainly carried out in the early morning, evening, or rainy weather. Therefore, this experimental comparison was performed in compliance with the Hemerocallis citrina Baroni picking. The comparison of the results of the YOLOv7 and GCB YOLOv7 models for the detection of Hemerocallis citrina Baroni maturity is shown in Figure 8.  
As can be seen from Figure 8a, the maturation recognition of Hemerocallis citrina Baroni in the early morning scene is more satisfactory. The YOLOv7 algorithm mistakenly recognizes plant rhizomes as Hemerocallis citrina Baroni with a recognition confidence of 0.48. Compared to the original algorithm, the improved GCB YOLOv7 algorithm does not show any misdetections in this image and has a higher detection confidence. In the cloudy sky scene for group (b), performing the same NMS strategy for the same Hemerocallis citrina Baroni, the YOLOv7 algorithm gives both 0.51 and 0.76 confidence, while the GCB YOLOv7 algorithm directly gives 0.93 confidence. The GCB YOLOv7 algorithm has a significant advantage over the NMS effect. Figure 7: F1 harmonized mean curve. In the cloudy and rainy weather scene for group (c), which is affected by the resolution of the detected images, especially the blurred edge resolution in the same image, the GCB YOLOv7 algorithm detected two Hemercollis citrina Baroni, while the original YOLOv7 algorithm detected only one Hemercollis citrina Baroni. Also, the YOLOv7 algorithm has a much lower detection confidence for the same Hemercollis citrina Baroni of only 0.36. In the evening scene for group (d), both the YOLOv7 and GCB YOLOv7 algorithms demonstrate excellent detection results for the single bouquet detection environment. How  
Figure 8: Test results. ever, the improved GCB YOLOv7 algorithm has better detection results, with confidence levels of 0.81 and 0.98 for the two Hemercollis citrina Baroni, which are higher than the original algorithm's 0.24 and 0.07, respectively. In addition, three different algorithms, YOLOv5, YOLOv7, and YOLOv8, were used to detect Hemercollis citrina Baroni maturity, respectively, and the experimental results are shown in Table 3.  
From Table 3, it can be seen that the floating-point operations of the YOLOv5 and YOLOv8 algorithms are larger compared to the YOLOv7 algorithm, which are 107.9 G and 165.4 G. At the same time, the real-time performance of the two algorithms is poorer, at only 64.16 FPS and 80.75 FPS, respectively. In addition, YOLOv5 has the highest accuracy but also the highest number of parameters, which may result in the need for a larger computing device for the Hemercollis citrina Baroni maturity detection device. In addition, comparative experiments replicated the models in the literature [29,30] and were conducted on the basis of the YOLOv5 standard model using the same yellow flower dataset. The experimental results show that, compared with the original YOLOv5, the CBAM-YOLOv5 model proposed in the literature [29] improves the detection efficiency, and the real-time performance improves from 64.16 FPS to 71.72 FPS, but the detection accuracy decreases to different degrees. The YOLO-CBAM model proposed in the literature [30], on the other hand, achieves a further improvement in performance but is slightly inferior to YOLOv7. Meanwhile, as can be seen from Table 3, the literature [29,30] improves on YOLOv5, while the proposed GCB YOLOv7 in this paper improves on the YOLOv7 algorithm.