## 1 Introduction
Main directions for improvement include adding local feature extraction modules [54], constructing point connection graphs [55; 56], applying the method of kernel point convolution [57; 58], implementing down-sampling strategies [56; 59; 60], increasing residual layers [61], and promoting multimodal feature fusion [62; 63]. Despite these advancements, these methods still utilize traditional featurizers that allow extensive progress in local feature extraction but fail to consider information from further points, thus hampering performance. In response, [PERSON] et al. [64] proposed the CurveNet model, a model pivoting on curves, which continually refines its trajectory to connect with more distant points and, as such, has demonstrated remarkable results. Acknowledging the escalating importance of attention mechanisms, certain researchers have adopted these strategies to foster enhanced interactions within point cloud data. For instance, [PERSON] et al. advocated for the PSegNet approach [59], which capitalized on channel and spatial attention to amplify the model's extraction capabilities. Furthermore, the advent of self-attention mechanisms and transformers has paved the way for exhilarating opportunities in the realm of point cloud data processing. Transformers inherently capture long-range relations via self-attention mechanisms [65]. Models such as the Point Transformer (PT) [66], Voxel Transformer (VT) [67], and Stratified Transformer (ST) [68] have demonstrated exceptional performance on numerous datasets, thereby drawing considerable interest in plant phenotyping research. Moreover, significant strides have been made by researchers such as [PERSON] et al., who integrated an Adaptive Self-Attention module into the PointNet framework, resulting in substantial improvements in performance [69]. Compellingly, some scholars have employed novel position encoding strategies [70] and stratified feature extraction architectures [71] in the classic transformer framework to fortify the model's attribute extraction ability. Undoubtedly, the emergence of self-attention mechanisms and transformer frameworks has significantly amplified the performance of point cloud segmentation models. However, this enhancement also incurs an appreciable computational cost. Moreover, models stemming from the transformer framework usually necessitate a substantial volume of training data. To address this predicament, certain researchers have gravitated towards weakly supervised segmentation techniques such as Eff-3 DPSeg [72]. Not only do these weakly supervised approaches reduce the reliance on extensive data, but they also provide fresh perspectives to enhance network segmentation performance. Despite the relatively comprehensive application of various point cloud segmentation algorithms in the realm of plant phenotyping, there are numerous issues necessitating further exploration. The intricate morphological structure of plants poses significant challenges; while some models demonstrate commendable segmentation performance on simpler plant entities, they struggle with segmenting complex mature crops or smaller organ categories. This predicament significantly impedes the progression of plant phenomics research. Therefore, whether these novel network models are suited to a variety of crop structures remains a worthwhile topic for exploration. Additionally, the majority of studies on plant organ segmentation methods are conducted on crop objects in controlled environments, with few extending to field environments. In plant cultivation and crop breeding, the ultimate aim of phenotyping is to promote vigorous plant growth in fields. Thus, technologies suitable for field phenotyping are needed. Finally, the quality of point clouds generated by various point cloud acquisition techniques differs; one cannot simply test model performance on point clouds acquired from a single platform. To some extent, data disparities also influence model performance.