## 5 Discussion
### The Impact of Model Composition  
In Table 2, three critical components of organ segmentation models, namely local operations, grouping methods, and featurizers, are summarized. Based on these components, an analysis of the performance differences among the models was conducted. Feasible recommendations for constructing segmentation models in the field of plant phenotyping were proposed. Discussing from the perspective of local operation, the goal of local operations is to encode information representation utilizing local geometric features. Two commonly used local search approaches are ball query and k-Nearest Neighbor (k-NN). Ball query entails random sampling of adjacent points within a predefined radius, while k-NN focuses on the nearest neighboring points. Consequently, for crops with significant local morphological deficiencies, random sampling within a fixed radius could often result in point loss around the local region, such as with PointNet++. This model exhibits commendable precision on point clouds with relatively intact morphology, such as Pheno4D, while it underperforms on objects with missing morphological structures. In facing this challenge, k-NN sustains favorable local features by opting for neighbors from the remaining points, similar to PAConv. However, in situations with an abundance of noise points, ball query often establishes recognition power superior to that of k-NN. This is because, for a point on an object, outliers are unlikely to fall within the querying sphere rather than to be its nearest neighbors [93]. In comparison, ST employs a method combining ball query and k-NN to acquire local features. It selects points based on their proximity to neighbor points within a set local radius, maintaining robustness and the completeness of local features simultaneously. Moreover, the dynamic assembly method was also noteworthy. PAConv demonstrated commendable performance in this validation due to its distinctive method of constructing convolution kernels. These were dynamically data-driven, with a weight matrix adaptively learned from points' positional relationships, enabling the model to manage plants' issues of inconsistent organ scale and irregularity in shape. A substantial performance enhancement was clearly observed in DGCNN following the integration of PAConv. The absence of point cloud morphological structures during data collection often poses challenges to achieving a comprehensive understanding of the models. However, two classical transformer-based models were less impacted by this issue. Compared to other models, transformer-based architectures, with their self-attention mechanism for feature aggregation, are capable of considering information from longer-range points. Specifically, even with partial local region occlusion, the transformer architecture can still capture points from other regions, maintaining the overall structural integrity. Other architectures have employed various strategies to achieve a wide receptive field. For instance, PointCNN maintains structural information integrity through multilayer stacked X-Conv kernels, which provide a global perspective. While CurveNet also attempts to capture more feature information by establishing long-range point-to-point relationships, it is hampered by the robustness of its scoring function, performing poorly on objects with significant structural information loss, such as maize point clouds reconstructed from UAV RGB images. Therefore, when designing an architecture to handle plant object data from 3D point clouds, the model's robustness and long-distance feature extraction capabilities still require careful consideration. Moreover, as indicated in Section 5.1, point cloud data of plant objects often present substantial class imbalance. Certain studies have attempted to rectify this problem via novel down-sampling approaches [60], yet the issue is not so simplistic. As plants grow, their organ scales also change, rendering the multiscale characteristics of plants another vital consideration.