## 4 Results
### Organ-Level Segmentation
#### 4.1.4 Comprehensive Evaluation
Post the final EdgeConv operation, it aggregates global features via max pooling. Despite its strengths, DGCNN's local information capture is limited by the scope of neighboring points it considers, omitting more distal points. CurveNet starts with the farthest point, employing a k-NN method and an MLP-based scoring system to select the starting point for curve analysis. It then uses a walking policy for curve construction and an MLP to extract and integrate curve features. CurveNet's advanced curve aggregation technique and walking strategy effectively incorporate data from distant points, enhancing connectivity across extended regions of the point cloud. Our comparative experiments indicate that CurveNet matches the performance of PointCNN and PAConv on the clean Pheno4D datasets, but its effectiveness declines on crop types collected outdoors. [PERSON] et al. have noted that CurveNet's curve detection is particularly vulnerable to noise, which can alter the initial curve point and cause inaccuracies [64]. In real-world conditions, the crop point clouds we collected contain noise and partial occlu \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **OA** & **mACC** & **mIoU** \\ \hline PointNet & 0.868 & 0.748 & 0.659 \\ PointNet++ & 0.910 & 0.804 & 0.734 \\ PointMLP & 0.897 & 0.829 & 0.722 \\ DGCNN & 0.906 & 0.811 & 0.733 \\ PointCNN & 0.921 & 0.835 & 0.766 \\ PAConv & 0.918 & 0.841 & 0.767 \\ CurveNet & 0.902 & 0.793 & 0.720 \\ PT & 0.927 & 0.865 & 0.801 \\ ST & **0.940** & **0.891** & **0.829** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The summary of segmentation results on all crops. The bolded text indicates optimal performance, while the underlined text denotes the second-best results. sions, significantly affecting CurveNet's ability to accurately group data. We conjecture that the robustness of CurveNet's performance is strongly dependent on dataset quality, with noise and missing data notably degrading its ability to segment plant objects. PointCNN and PAConv demonstrate acceptable segmentation abilities for various plants, including rapeseed, potatoes, and cotton, with similar performance outcomes. PointCNN features a unique convolutional operator called X-Conv, which utilizes k-NN to collect nearby points for feature aggregation, adapting the principles of traditional CNNs for 3D point cloud data. The X-Conv layers gather and centralize features from adjacent points, and through their sequential arrangement, they progressively widen their receptive field to cover the entire point cloud. While this method effectively captures and combines local features, it struggles to integrate attributes from more distant points. This shortcoming is notably problematic when analyzing crops with complex morphologies, where the model's reliance on stacking convolutional layers might fail to capture detailed feature interactions, possibly resulting in less accurate segmentation. The Position-Adaptive Convolution (PAConv) method optimizes point cloud processing by employing k-NN to select neighboring points and dynamically creating a weight matrix using a network named ScoreNet. This adaptability allows the operator to conform to diverse point cloud geometries effectively. Integrating PAConv into the DGCNN framework, and replacing the EdgeConv operator, significantly enhances the model's ability to capture local features. This integration reduces the computational load of measuring feature distances and facilitates the inclusion of additional neighboring points, thereby improving local feature representation. As a result, there is a notable increase in the average Intersection over Union (IoU) across various crops, from 0.733 to 0.767. The application of PAConv within the DGCNN framework enables superior segmentation, particularly in relatively complex crops like cotton, marking a substantial improvement in the detailed capture of local structures within point clouds.