# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 3 Experimental Results and Analysis
### Semantic Segmentation Results  
To intuitively evaluate the semantic segmentation performance of TSINet on tomato plant point clouds, we visualized part of the segmentation results, as shown in Figure 6. The tomato is a dicotyledonous plant with a complex canopy structure. As illustrated, TSINet demonstrates excellent segmentation performance, with most of the leaf and stem structures accurately classified. Only a few points near boundary regions were misclassified, which may be attributed to the inherent ambiguity of such transitional areas.  
We further conducted a comparative analysis between TSINet and several state-of-the-art deep learning-based point cloud semantic segmentation models, including PointNet [28], PointNet++ [24], DGCNN [26], ASIS [29], and JSNet [30]. The quantitative results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Part** & **PointNet [28]** & **PointNet++ [24]** & **DGCNN [26]** & **ASIS [29]** & **JSNet [30]** & **TSINet (Ours)** \\ \hline \multirow{3}{*}{Precision (\%)} & Leaf & 95.35 & 97.16 & 97.35 & 97.27 & 97.34 & 97.89 \\  & Stem & 96.10 & 94.99 & 95.53 & 95.19 & 95.27 & 96.10 \\ \cline{2-8}  & Mean & 95.72 & 96.07 & 96.44 & 96.23 & 96.30 & 97.00 \\ \hline \multirow{3}{*}{Recall (\%)} & Leaf & 98.85 & 98.40 & 98.57 & 98.47 & 98.49 & 98.74 \\  & Stem & 85.46 & 91.32 & 91.91 & 91.67 & 91.88 & 93.59 \\ \cline{2-8}  & Mean & 92.15 & 94.86 & 95.24 & 95.07 & 95.18 & 96.17 \\ \hline \multirow{3}{*}{F1-score (\%)} & Leaf & 97.07 & 97.78 & 97.96 & 97.87 & 97.91 & 98.32 \\  & Stem & 90.47 & 93.12 & 93.69 & 93.40 & 93.54 & 94.83 \\ \cline{2-8}  & Mean & 93.77 & 95.45 & 95.82 & 95.63 & 95.73 & 96.57 \\ \hline \multirow{3}{*}{IoU (\%)} & Leaf & 94.30 & 95.65 & 96.00 & 95.82 & 95.90 & 96.69 \\  & Stem & 82.59 & 87.12 & 88.13 & 87.62 & 87.87 & 90.17 \\ \cline{1-1} \cline{2-8}  & Mean & 88.45 & 91.39 & 92.06 & 91.72 & 91.89 & 93.43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different semantic segmentation models.  
Figure 6: Visualization of semantic segmentation results by TSINet. In the color legend, red denotes leaf regions, and green denotes stem regions. Sem.GT refers to the point clouds of plants annotated with semantic ground truth labels.  
As the earliest deep learning method for point clouds, PointNet exhibits lower overall performance, especially with poor recall and IoU for the stem category. Subsequent methods such as PointNet++, DGCNN, ASIS, and JSNet show progressively better results, indicating that improvements in network architectures and feature extraction capabilities greatly enhance segmentation performance. TSINet outperformed all baseline methods across all evaluation metrics. Specifically, it achieved 97.00% in precision, 96.17% in recall, 96.57% in F1-score, and 93.43% in mean Intersection-over-Union (IoU), demonstrating its superior capability in extracting accurate semantic features from the complex geometry of tomato plant point clouds. Furthermore, the segmentation metrics for the leaf category are consistently higher than those for the stem category, suggesting that leaf segmentation is easier, while stem segmentation is more challenging due to its fewer points or more complex morphology. Notably, TSINet shows remarkable improvements in the stem category, achieving a recall of 93.59% and an IoU of 90.17%, highlighting its strong ability to handle small or difficult-to-segment structures and its robustness in differentiating complex plant geometries.