# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 4 Experimental Results and Analysis  
### Performance Comparison of CGFM Insertion Positions  
Four candidate positions were identified for integrating the CGFM module within the RTDETR feature fusion network, indicated by the boxes numbered 1-4 in Figure 9. These positions were evaluated individually and in combination according to the controlled variable principle, resulting in the 15 configurations shown in Table 7; the table also reports the corresponding recognition performance metrics for each configuration. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Setting** \\ \hline Input image size & 640 \(\times\) 640 \\ Initial learning rate & 0.0001 \\ Momentum & 0.9 \\ Weight decay coefficient & 0.0001 \\ IoU threshold & 0.7 \\ Batch-size & 4 \\ Training epochs & 200 \\ \hline \end{tabular}
\end{table} 
Table 6: Experimental hyperparameter settings. The experimental data reported in Table 7 indicate that multi-position configurations achieved moderate accuracy gains compared to single-position configurations (e.g., a +1.1% improvement in \(R\)), but their comprehensive performance metrics, particularly _mAP50:95_ and \(F1\), underperformed the single-position configurations by 0.8-1.8%. In terms of detection, the LiteTom-RTDETR-CGFM-1+(2) configuration achieved a \(P\) of 88.8%, \(R\) of 78.4%, \(F1\) of 86.6%, _mAP50_ of 61.3%, and _mAP50:95_ of 83.3%, corresponding to marginal performance differentials of \(-0.2\%\), \(-1.7\%\), \(+0.2\%\), \(+0.3\%\), and \(-1.0\%\), respectively, compared to the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \(P\) & \(R\) & _mAP50_ & _mAP50:95_ & **F1** \\ \hline LiteTom-RTDETR & 89.0 & 80.1 & 86.4 & 61.0 & 84.3 \\ LiteTom-RTDETR-CGFM-1 & 89.1 & 80.4 & 87.0 & **62.7** & 84.5 \\ LiteTom-RTDETR-CGFM-2 & 88.2 & 80.5 & 87.1 & 62.3 & 84.2 \\ LiteTom-RTDETR-CGFM-3 & 89.6 & 81.1 & **88.2** & 61.9 & **85.1** \\ LiteTom-RTDETR-CGFM-4 & 87.2 & 79.6 & 86.3 & 61.4 & 83.2 \\ LiteTom-RTDETR-CGFM-4 & 88.8 & 78.4 & 86.6 & 61.3 & 83.3 \\ LiteTom-RTDETR-CGFM-1 & 3 & 87.1 & 79.5 & 86.3 & 61.1 & 83.1 \\ LiteTom-RTDETR-CGFM-1 & 4 & 88.5 & 78.7 & 86.6 & 62.4 & 83.3 \\ LiteTom-RTDETR-CGFM-2 & 3 & 88.0 & 79.6 & 86.5 & 62.4 & 83.6 \\ LiteTom-RTDETR-CGFM-2 & 4 & 88.4 & 80.1 & 86.8 & 61.2 & 84.0 \\ LiteTom-RTDETR-CGFM-3 & 4 & 87.1 & 80.3 & 86.5 & 62.3 & 83.6 \\ LiteTom-RTDETR-CGFM-1 & 2 & 3 & 86.6 & **81.2** & 86.9 & 61.2 & 83.8 \\ LiteTom-RTDETR-CGFM-1 & 2 & 4 & 89.3 & 79.8 & 86.8 & 62.3 & 84.3 \\ LiteTom-RTDETR-CGFM-1 & 3 & 4 & 88.3 & 79.9 & 87.1 & 61.6 & 83.9 \\ LiteTom-RTDETR-CGFM-2 & 3 & 4 & 88.1 & 79.3 & 86.2 & 60.5 & 83.5 \\ LiteTom-RTDETR-CGFM-1 & 2 & 3 & 4 & 88.7 & 79.0 & 86.6 & 61.2 & 83.6 \\ \hline \hline \end{tabular} Note: Bold values indicate the optimal results.  
\end{table} 
Table 7: Comparative performance of models with different CGFM insertion points (%). Figure 9: Schematic diagram of CGFM insertion points in the feature fusion network, where S3 represents stage 3 feature map from the backbone network; S4 represents stage 4 feature map from the backbone network; F5 represents processed feature map from stage 5 after AIFI module. baseline LiteTom-RTDETR. The expanded LiteTom-RTDETR-CGFM-1+2+3 configuration achieved a \(P\) of 86.6%, \(R\) of 81.2%, _F1_ of 86.9%, _mAP50_ of 61.2%, and _mAP50:95_ of 83.8%, corresponding to performance differentials of \(-\)2.4%, +1.1%, +0.5%, +0.2%, and \(-\)0.5%, respectively, compared to the baseline model. The single-position LiteTom-RTDETR-CGFM-3 configuration exhibited the best performance among the evaluated configurations, with a \(P\) of 89.6%, \(R\) of 81.1%, _F1_ of 88.2%, _mAP50_ of 61.9%, and _mAP50:95_ of 85.1%, corresponding to consistent improvements of +0.6%, +1.0%, +1.8%, +0.9%, and +0.8%, respectively, compared to the baseline model. Notably, the \(P\), _F1_, and _mAP50_ values for this configuration were the highest among the considered experimental configurations.