# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 4 Experiments
### Comparison with Different Network Models  
Systematic comparative experiments conducted between the proposed model and mainstream object detection algorithms, with the results shown in Table 2. The experimental results demonstrate that the proposed YOLO-PGC method exhibits significant advantages in the tomato maturity detection task. In terms of detection accuracy, our model achieved an mAP of 81.6% and precision of 80.4%, clearly outperforming mainstream algorithms such as YOLOv3 (76.9%), YOLOv5 (79.6%), and YOLOv6 (72.3%). Notably, even when compared to YOLOv5 and YOLOv8, which have fewer parameters, our proposed YOLO-PGC still maintains a distinct performance advantage. In terms of computational efficiency, our model requires only 3.8 M parameters and 8.7 G FLOPs, demonstrating a superior efficiency-to-performance ratio compared to YOLOv9 (7.1 M parameters, 26.7 G FLOPs) and RTDETR (4.2 M parameters, 125.6 G FLOPs). These results strongly confirm that our proposed method achieves high detection accuracy while maintaining excellent computational efficiency, making it more suitable for deployment in practical agricultural scenarios. Experimental Results: The YOLOv11 model integrated with the PSSD module achieved significant improvements across multiple key performance metrics. As shown in Table 3, the mAP and mAP50 of YOLOv11+PSSD reached 80.9% and 65.1%, respectively, an increase of 2.1 and 2.4 percentage points compared to the baseline YOLOv11 model. Notably, in terms of precision (P), the model performance improved to 82.1%, which is a 6.4 percentage point increase from the original model, demonstrating superior target localization capability. Although the recall (R) slightly decreased by 0.6 percentage points, this tradeoff is acceptable considering the significant improvement in precision. It is also worth noting that the PSSD module outperformed other improvement strategies (C2 FPSA and PSA) in terms of performance gains. While the parameter counts and computational load slightly increased (parameter count by 0.9 M and FLOPs by 0.7 G), the substantial performance improvement justifies this increase in computational overhead. These results strongly demonstrate the effectiveness and superiority of the PSSD module in enhancing object detection performance. The experimental results demonstrate that the proposed CIF2M module achieves remarkable success in balancing performance and efficiency. As shown in Table 4, YOLOv11 with the CIF2M module shows overall improvements in key metrics; the mAP reaches 80.4% (a 1.6 percentage point improvement) and mAP50 increases to 64.6% (a 1.9 percentage point increase). Notably, this module exhibits outstanding performance in both precision (P) and recall (R), achieving 80.8% and 72.1%, respectively. These results correspond to improvements of 5.1 and 0.6 percentage points compared to the baseline model, demonstrating stronger object detection capabilities. Compared to other improvement schemes, the CIF2M module stands out with a significant overall advantage. While the C2f scheme shows similar performance in terms of mAP (80.2%), its FLOPs reach as high as 8.8 G. In contrast, CIF2M achieves superior performance with only 6 G of computational load. Moreover, the parameter count (2.7 M) remains relatively moderate, increasing by just 0.1 M compared to the baseline model, which is significantly lower than the parameter overhead of the C2f scheme. These results indicate that CIF2M not only improves the performance of the model but also effectively balances computational efficiency and resource consumption, showcasing its excellent practical value for engineering applications. The experimental results show that incorporating the GHVC module into the neck part of YOLOv11 significantly improves the overall performance of the model. As shown in Table 5, YOLOv11+GHVC achieves remarkable progress in several detection accuracy metrics; the mAP reaches 80.2%, a 1.4 percentage point improvement over the baseline model, and the mAP50 increases to 64.4%, a 1.7 percentage point improvement.