## 6 Conclusions  
We conducted in-depth comparative experimental analyses on several typical deep learning models. To evaluate the models' segmentation performance across diverse environments and data generated by different platforms, we have summarized the existing public datasets, provided detailed descriptions, and identified the shortcomings of the current datasets: limited plant species, small sample size, single data collection method, and overly ideal collection environment. We conducted data acquisition using terrestrial laser scanning and UAV laser scanning, and reconstructed point clouds from RGB images. The datasets comprised point cloud data from various crops, including maize, cotton, potato, and rapeseed. These datasets were subsequently utilized to assess the models' practical application effectiveness.  
At the organ-level segmentation, point cloud data acquisition and generation can be divided into five categories: indoor laser scanning, indoor 3D reconstruction from images, outdoor potted terrestrial laser scanning, field terrestrial laser scanning, and 3D reconstruction from UAV RGB images. The experiments show that ST and PT have achieved the best and second-best results, respectively, but require significant computational resources. PAConv and PointCNN have great potential on crop objects with simpler morphological structures. Based on the experimental results of the nine typical models, we have conducted in-depth experiments and comparative analyses of the models' performance, focusing on three key steps of model composition: local operations, grouping methods, and feature aggregation methods. In this way, we have explored the reasons for the advantages and disadvantages of model performance and identified the inherent challenges of plant organ segmentation tasks: imbalanced class data, data loss caused by occlusion, and unavoidable noise.  
Additionally, the potential application of the classic scene instance segmentation model Mask3D on maize point clouds collected by UAV-LS was validated. Mask3D achieved commendable performance, with a mean Average Precision (mAP) of 0.909. This finding supports the development of automated segmentation methods from plot point clouds to individual plant point clouds. Unlike traditional point cloud instance segmentation methods, Mask3D does not require the pre-computation of complex features, which greatly facilitates the segmentation of field maize. Research on end-to-end algorithms for segmenting individual point clouds from plot point clouds remains relatively scarce and warrants further exploration.  
In future research, several key points warrant attention. First, the lack of automated methods for segmenting individual plant point clouds from plot point clouds limits high-throughput phenotyping in field scenarios. Second, robust point cloud segmentation models with strong feature extraction capabilities and the ability to adapt to multi-scale information are required. Additionally, in the realm of deep learning models, the down-sampling of point clouds presents a critical issue. The number of input points is linearly related to the model's computational cost. Therefore, determining an appropriate threshold is a topic that merits investigation.  
[PERSON] designed the work and wrote the manuscript. [PERSON] performed experiments, analyzed the data, and wrote the manuscript. [PERSON], [PERSON] and [PERSON] performed experiments and analyzed the data. [PERSON], [PERSON] and [PERSON] revised the manuscript. [PERSON] supervised the project. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2023 YFF1000100, 2022 YFD2002304).  
Data Availability Statement: The point cloud data in this study are available upon request by contacting the corresponding author.