# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 4 Discussion  
### Ablation Study  
In order to assess how well the main TSINet components work, specifically the Geometry-Aware Adaptive Feature Extraction Block (GAFEB) and the Dual Attention-Based Feature Enhancement Module (DAFEM), we conducted two groups of ablation experiments. In particular, the ablation of DAFEM (Group A) was conducted by directly removing its parallel attention mechanisms, namely the channel attention (CA) and spatial attention (SA). For ablation of the GAFEB (Group B), we replaced the EdgeConv and PAConv operations with standard multi-layer perceptrons (MLPs), while maintaining the overall network architecture to ensure fair comparisons. Table 4 presents the semantic segmentation performance of each group. The results show that removing DAFEM (Group A) led to a consistent decline in all evaluation metrics compared to the full model (Group C), with a particularly notable drop in the \"Stem\" category. For example, the IoU for Stem decreased from 90.17% to 87.20%, indicating that the dual attention module plays a crucial role in accurately segmenting plant organs with complex structures and ambiguous boundaries. Similarly, replacing EdgeConv and PAConv in GAFEB with MLPs (Group B) also resulted in a significant performance degradation. For instance, the mean IoU dropped from 93.43% to 90.16%, and the F1-score decreased from 96.57% to 94.76%. These results suggest that, even when the network architecture remains unchanged, MLPs alone are insufficient for effectively capturing the local geometric features and global spatial structures inherent in point cloud data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Group**} & \multicolumn{3}{c}{**Component**} & \multirow{2}{*}{**Leaf**} & \multirow{2}{*}{**Stem**} & \multirow{2}{*}{**Mean**} \\  & & & **GAFEB** & & & & \\ \hline \multirow{2}{*}{Precision (\%)} & A & \(\surd\) & \(\times\) & 97.15 & 95.11 & 96.13 \\  & B & \(\times\) & \(\surd\) & 96.91 & 93.63 & 95.27 \\  & C & \(\surd\) & \(\surd\) & 97.89 & 96.10 & 97.00 \\ \hline \multirow{2}{*}{Recall (\%)} & A & \(\surd\) & \(\times\) & 98.44 & 91.29 & 94.86 \\  & B & \(\times\) & \(\surd\) & 97.96 & 90.57 & 94.26 \\  & C & \(\surd\) & \(\surd\) & 98.74 & 93.59 & 96.17 \\ \hline \multirow{2}{*}{F1-score (\%)} & A & \(\surd\) & \(\times\) & 97.79 & 93.16 & 95.48 \\  & B & \(\times\) & \(\surd\) & 97.43 & 92.08 & 94.76 \\  & C & \(\surd\) & \(\surd\) & 98.32 & 94.83 & 96.57 \\ \hline \multirow{2}{*}{IoU (\%)} & A & \(\surd\) & \(\times\) & 95.68 & 87.20 & 91.44 \\  & B & \(\times\) & \(\surd\) & 94.99 & 85.32 & 90.16 \\ \cline{1-1}  & C & \(\surd\) & \(\surd\) & 96.69 & 90.17 & 93.43 \\ \hline \hline \end{tabular} Note: “\(\surd\)” indicates the component is included; “\(\times\)” indicates the component is excluded.  
\end{table} 
Table 4: Ablation study of semantic segmentation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mPrec (\%)** & **mRec (\%)** & **mCov (\%)** & **mWCov (\%)** \\ \hline \hline ASIS [29] & 72.20 & 67.92 & 70.92 & 74.62 \\ JSNet [30] & 74.61 & 70.04 & 72.59 & 79.12 \\ TSINet (ours) & 81.54 & 81.69 & 81.60 & 86.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different instance segmentation models. In summary, the complete model (Group C) outperformed both ablated versions across all metrics, demonstrating the complementary and effective roles of GAFEB and DAFEM in feature extraction. Their integration significantly enhances the semantic segmentation performance for both leaf and stem components. In line with the semantic segmentation results, the instance segmentation performance reported in Table 5 further confirms the effectiveness and complementary nature of GAFEB and DAFEM. The complete model (Group C) achieved the best overall results, with consistently superior performance across all evaluation metrics. These findings underscore the pivotal role of the proposed modules in improving TSINet's capability for accurate 3D plant instance segmentation.