## 4 Results
### Organ-Level Segmentation
#### 4.1.3 Experiments on Image-Generated Point Cloud  
The model's segmentation performance was validated using point clouds of reconstructed tomato plants as the dataset. This dataset was obtained under greenhouse conditions using DSLR cameras to capture RGB images, which were then reconstructed. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Maize\_1**} & \multicolumn{2}{c}{**Cotton**} & \multicolumn{2}{c}{**Potato**} & \multicolumn{2}{c}{**Rapeseed**} \\
**OA** & **mACC** & **mIoU** & **OA** & **mACC** & **mIoU** & **OA** & **mACC** & **mIoU** & **OA** & **mACC** & **mIoU** \\ \hline PointNet & 0.857 & 0.598 & 0.527 & 0.743 & 0.746 & 0.626 & 0.933 & 0.792 & 0.702 & 0.927 & 0.797 & 0.727 \\ PointNet++ & 0.929 & 0.731 & 0.676 & 0.811 & 0.813 & 0.705 & 0.957 & 0.881 & 0.799 & 0.936 & 0.805 & 0.742 \\ PointMLP & 0.909 & 0.766 & 0.633 & 0.821 & 0.822 & 0.716 & 0.954 & 0.835 & 0.773 & 0.922 & 0.809 & 0.721 \\ DGCNN & 0.912 & 0.701 & 0.620 & 0.804 & 0.806 & 0.696 & 0.974 & 0.914 & 0.865 & 0.938 & 0.845 & 0.768 \\ PointCNN & 0.934 & 0.744 & 0.683 & 0.845 & 0.846 & 0.749 & 0.968 & 0.880 & 0.836 & 0.938 & 0.819 & 0.750 \\ PAConv & 0.925 & 0.736 & 0.668 & 0.824 & 0.825 & 0.719 & 0.972 & 0.901 & 0.855 & 0.950 & 0.844 & 0.777 \\ CurveNet & 0.926 & 0.721 & 0.660 & 0.818 & 0.820 & 0.713 & 0.959 & 0.869 & 0.800 & 0.939 & 0.830 & 0.764 \\ PT & 0.945 & 0.806 & 0.751 & 0.837 & 0.839 & 0.739 & 0.974 & 0.912 & 0.867 & 0.947 & **0.859** & 0.785 \\ ST & **0.950** & **0.890** & **0.806** & **0.878** & **0.879** & **0.794** & **0.988** & **0.948** & **0.923** & **0.959** & 0.857 & **0.799** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation results on plants from agricultural scenarios. Maize_1 refers to the maize data collected in field scenarios using TLS. The bolded text indicates optimal performance, while the underlined text denotes the second-best results. Figure 5: Segmentation results of maize and cotton collected from real planting scenarios. The circled areas indicate incorrectly segmented regions; GT represents the ground truth. Table 5 presents the segmentation results of the models on a dataset of mature tomatoes, with the visualization results displayed in Figure 6. The morphological structure of the tomato plants is highly complex, compounded by a few persistent noise points that increase the difficulty of organ segmentation. The mean Intersection over Union (mIoU) for all models was below 70%. ST and PT achieved the best and second-best results, respectively, but the segmentation outcomes were still unsatisfactory. The models continue to struggle with the segmentation of tomato fruits, and the segmentation of stems and leaves in densely leafed areas also poses significant challenges. Other models even failed to identify the majority of the tomato stems. Additionally, to verify the potential of the model in the segmentation task of point clouds derived from UAV-based RGB images, we utilized reconstructed maize point clouds as an example dataset. The model performance is summarized in Table 6, with the visualization results displayed in Figure 7. All models achieved an accuracy above 0.8.