# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 4 Experiments
### Ablation Experiment  
To systematically assess the performance contribution of the CIF2M and GHVC modules on the tomato maturity detection task, detailed ablation experiments were designed, as shown in Table 7. In these experiments, each core component was added sequentially to the baseline model, after which we conducted in-depth analyses of how the different module configurations affected the performance of the proposed model.  
When using YOLOv11 as the baseline model (mAP: 78.8%, precision: 71.5%), the performance improved significantly after introducing the PSSD module (mAP: 80.9%,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Size** & **AP** & **Params** & **FLOPS** \\ \hline YOLOv3 [8] & 640 x 640 & 33.0 & 12.1 M & 18.9 B \\ YOLOv5 [50] & 640 x 640 & 28.0 & 2.5 M & 7.1 B \\ YOLOv6 [51] & 640 x 640 & 37.0 & 4.2 M & 11.8 B \\ YOLOv8 [9] & 640 x 640 & 37.3 & 3.0 M & 8.1 B \\ YOLOv10 [33] & 640 x 640 & 38.5 & 2.7 M & 8.4 B \\ YOLOv11 [13] & 640 x 640 & 39.5 & 2.6 M & 6.5 B \\ Ours & 640 x 640 & 40.9 & 3.8 M & 8.7 B \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different models on the COCO dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **mAP** & **mAP50** & **P** & **R** & **Parameters (M)** & **FLOPs (G)** \\ \hline YOLOv11 & 78.8 & 62.7 & 75.7 & 71.5 & 2.6 & 6.5 \\ YOLOv11 + C2 fCIB [9] & 79.2 & 62.3 & 75.9 & 71.8 & 2.5 & 7.3 \\ YOLOv11 + C2f [9] & 80.2 & 64.4 & 79.6 & 71.7 & 2.8 & 8.8 \\ YOLOv11 + C3 Ghost [53] & 79.8 & 63.4 & 77.2 & 71.0 & 2.4 & 6.2 \\ YOLOv11 + C3 Ghost [53] & 79.8 & 63.4 & 77.2 & 71.0 & 2.4 & 6.2 \\ YOLOv11 + CHVC & 80.2 & 64.4 & 78.2 & 72.7 & 2.7 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of CIF2M backbone module.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **mAP** & **mAP50** & **P** & **R** & **Parameters (M)** & **FLOPs (G)** \\ \hline YOLOv11 & 78.8 & 62.7 & 75.7 & 71.5 & 2.6 & 6.5 \\ YOLOv11 + C2 fCIB [9] & 79.2 & 62.3 & 75.9 & 71.8 & 2.5 & 7.3 \\ YOLOv11 + C2f [9] & 80.2 & 64.4 & 79.6 & 71.7 & 2.8 & 8.8 \\ YOLOv11 + C3 Ghost [53] & 79.8 & 63.4 & 77.2 & 71.0 & 2.4 & 6.2 \\ YOLOv11 + C3 Ghost [53] & 79.8 & 63.4 & 77.2 & 71.0 & 2.4 & 6.2 \\ YOLOv11 + CHVC & 80.2 & 64.4 & 78.2 & 72.7 & 2.7 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of GHVC neck module.  
precision: 70.9%), validating the effectiveness of this module in feature extraction. When the CIF2M and GHVC modules were added together, the model achieved optimal performance (mAP: 81.6%, precision: 74.6%). In contrast, combinations such as CIF2M (mAP: 81.1%, precision: 72.9%) and PSSD+GHVC (mAP: 81.0%, precision: 70.6%) showed improvements, but were not as effective as the complete architecture.  
It is worth noting that using only GHVC (mAP: 80.2%, precision: 72.7%) or CIF2M (mAP: of 80.4%, precision: 72.1%) can still improve performance, although there is a significant gap when compared to the optimal configuration. In terms of computational efficiency, the complete model (YOLOv11+PSSD+CIF2M+GHVC) only needs 3.8 M parameters and 8.7 G FLOPs, showing excellent efficiency and performance.  
The experimental results fully confirm the synergistic advantages of the CIF2M and GHVC modules, showing especially strong robustness in scenarios with complex backgrounds and occlusion. The proposed design not only achieves breakthroughs in detection accuracy but also offers excellent computational efficiency, making it highly feasible for real-world deployment.