## 4 Results
### Organ-Level Segmentation
#### 4.1.4 Comprehensive Evaluation  
The measurement approach comprehensively evaluates the models' performance on both the ideal Pheno4D data and datasets collected under real-world conditions. We aim to use this test to assess the segmentation performance of the models across various types of \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **OA** & **mACC** & **mIoU** & \multicolumn{3}{c}{**Soil**} & \multicolumn{3}{c}{**Stem**} & \multicolumn{3}{c}{**Leaf**} \\ \cline{4-10}  & & & & **IoU** & **Acc** & **IoU** & **Acc** & **IoU** & **Acc** \\ \hline PointNet & 0.890 & 0.864 & 0.761 & 0.960 & 0.997 & 0.529 & 0.747 & 0.795 & 0.849 \\ PointNet++ & 0.928 & 0.896 & 0.827 & 0.971 & **0.998** & 0.647 & 0.764 & 0.864 & 0.926 \\ PointMLP & 0.871 & 0.837 & 0.732 & 0.917 & 0.939 & 0.506 & 0.701 & 0.772 & 0.872 \\ DGCNN & 0.926 & 0.906 & 0.828 & 0.967 & 0.990 & 0.656 & 0.817 & 0.861 & 0.910 \\ PointCNN & 0.918 & 0.890 & 0.810 & 0.980 & 0.994 & 0.606 & 0.770 & 0.844 & 0.907 \\ PAConv & 0.928 & 0.902 & 0.828 & 0.980 & 0.995 & 0.644 & 0.792 & 0.860 & 0.917 \\ CurveNet & 0.838 & 0.695 & 0.608 & 0.952 & 1.000 & 0.128 & 0.144 & 0.743 & **0.940** \\ PT & 0.938 & 0.922 & 0.851 & 0.982 & 0.996 & 0.692 & 0.849 & 0.880 & 0.920 \\ ST & **0.949** & **0.944** & **0.877** & **0.984** & 0.995 & **0.746** & **0.915** & **0.900** & 0.922 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Segmentation results on maize point clouds derived from UAV images. The bolded text indicates optimal performance, while the underlined text denotes the second-best results. Figure 7: Segmentation results of maize reconstructed by airborne RGB images. The circled areas indicate incorrectly segmented regions; GT represents the ground truth. datasets. As shown in Table 7, ST and PT achieved the best and second-best results, respectively. PointCNN and PAConv reached an mIoU of over 75%, followed by PointNet++ and DGCNN. CurveNet and PointMLP exhibited similar segmentation outcomes, performing poorly on crop subjects with insufficient precision in detailed segmentation. PointNet was unable to handle the organ segmentation tasks for the aforementioned subjects adequately. PointNet, a pivotal model in point cloud data processing, utilizes a unique point-wise approach and architecture as detailed in Section 3.2. Despite its innovation, PointNet does not consider inter-point dynamics and primarily depends on global feature extraction through max pooling at the terminal stage. Nonetheless, PointNet lacks power in tasks requiring both local and global feature integration for point cloud segmentation. Its inability to simultaneously capture detailed local structures and overarching global patterns makes it less effective for segmenting plant organs, where intricate local-global feature extraction is crucial. PointNet++ significantly enhances local feature extraction compared to its predecessor, PointNet. It adopts an encoder-decoder framework that leverages FPS to identify key central points. These points anchor a ball search that divides the point cloud into overlapping local areas via a grouping process. Within these areas, a mini-PointNet was used to encode local region patterns into feature vectors. This design enables PointNet++ to efficiently gather a more comprehensive set of local data, improving its utility in detailed segmentation tasks. PointMLP is noted for its efficient design, incorporating residual point MLP modules for local feature extraction and max pooling for feature aggregation. This model, inspired by residual networks, progressively expands its receptive field, similar to PointNet++, which also uses k-NN for local tasks and MLP for feature processing. Although PointMLP successfully enlarges its receptive field by layering MLP modules to enhance global feature capture, it falls short in integrating local group interconnections. DGCNN utilizes a graph-based framework to delve into complex point relationships within a graph. DGCNN incorporates k-NN aggregation in its EdgeConv module to capture local details.