# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 4 Experimental Results and Analysis
### Ablation Experiments  
Ablation experiments were systematically conducted to quantitatively evaluate the effectiveness of the individual model modules under the controlled variable principle. The considered experimental conditions are described along with their comprehensive performance metrics in Table 8. This methodological approach sequentially evaluated the three architectural modifications: first, the baseline backbone network was replaced with RepViT to enhance computational efficiency; second, the CGFM mechanism was integrated at position 3 to refine the feature fusion capabilities; third, the SVFL function was implemented to improve detection accuracy.  
The results for Groups 1 and 2 in Table 8 indicate that the replacement of the original backbone with RepViT significantly reduced the model size and GFLOPs by 31.6% and 36.2%, respectively. This reduction in complexity stems from the lightweight architecture of RepViT, which optimized computational efficiency.  
The Group 3 and 5 results indicate that introducing the CGFM enhanced the _mAP50_ by 1.8% compared to Group 2, with marginal increases in the number of parameters (+0.3%) and inference speed (+0.9 FPS). These performance improvements stem from the adaptive multiscale feature fusion mechanism in the CGFM, which enhanced the contextual awareness of the agricultural targets through hierarchical feature interactions. Notably, the use of the CGFM achieved superior detail preservation in this complex field environment while maintaining computational efficiency, which is a critical requirement for real-time agricultural vision systems. The heatmap analysis shown in Figure 10 demonstrates the enhanced feature fusion capability of the improved CGFM module. Both green and red fruits exhibited concentrated attention regions in their lower-middle sections, indicating  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Backbone**} & \multirow{2}{*}{**CGFM**} & \multirow{2}{*}{**VFL**} & \multirow{2}{*}{**SVFL**} & _mAP50_ & **Parameters** & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**Inference**} & \multirow{2}{*}{**Model**} \\ \cline{2-2} \cline{6-9}  & **ResNet18** & & & & & & & **(\%)** & & **(Millions)** & \\ \hline
1 & \(\surd\) & — & — & \(\surd\) & — & 87.6 & 19.97 & 56.9 & 45.2 & 38.6 \\
2 & — & \(\surd\) & — & \(\surd\) & — & 86.2 & 13.30 & 36.3 & 52.5 & 26.4 \\
3 & — & \(\surd\) & — & — & \(\surd\) & 86.4 & 13.30 & 36.3 & 52.3 & 26.4 \\
4 & — & \(\surd\) & \(\surd\) & \(\surd\) & — & 87.0 & 13.34 & 36.3 & 51.8 & 26.4 \\
5 & — & \(\surd\) & \(\surd\) & — & \(\surd\) & 88.2 & 13.34 & 36.3 & 53.2 & 26.4 \\ \hline \hline \end{tabular} Note: The symbol “\(\surd\)” indicates that the module is activated, whereas the symbol “—” indicates that it is deactivated.  
\end{table}
Table 8: Results of ablation experiment.  
robust multiscale object recognition that effectively avoided misclassifying green foliage as immature fruit.  
Finally, the loss curves presented in Figure 11 demonstrate that the incorporation of the SVFL function significantly accelerated the convergence rates compared with the baseline VFL configuration while achieving superior final loss metrics, confirming its effectiveness in dynamic training optimization for agricultural target detection tasks.  
Figure 11: Comparison of training loss convergence for Groups 4 and 5.  
Figure 10: Visualization of the CGFM effect using heatmaps.