## 1 Introduction  
The automatic phenotypic analysis of plants based on computer vision technology has become a crucial component in facilitating high-throughput experimentation in both botany and agricultural research. Compared to traditional manual measurement methods, computer vision-based phenotyping methodologies offer advantages in reducing manual labor, promoting non-invasiveness, and efficiently managing large volumes of data [1]. As a result, these methodologies have been widely recognized and applied over the past several years. This morphological analysis technique can be divided into 2D image-basedphenotyping methods and 3D point cloud-based phenotyping, depending on the data dimensionality used. While 2D phenotyping technology has been actively pursued by many researchers due to its convenient data acquisition and high-throughput capabilities, the persisting challenge of plant occlusion, primarily due to the lack of spatial structural information within 2D images, remains. To overcome these challenges, the research community has turned to three-dimensional data processing techniques in phenotypic studies, with a notable focus on employing 3D point cloud data for detailed phenotypic analysis [2; 3; 4; 5]. Unlike 2D imaging, 3D imaging technologies capture and convey spatial information inherently in three dimensions. These methodologies have the potential to significantly reduce information loss from data occlusion and overlap, offering a promising outlook for phenotypic data acquisition. Consequently, the integration of 3D vision technologies into plant phenotyping research has become more prevalent, enabling more detailed and comprehensive phenotypic evaluations. Methods for obtaining plant point clouds can be systematically categorized as either active or passive. Active measurement techniques involve the direct manipulation of sensors to capture the 3D structure of plants [6]. Common 3D sensor measurement techniques include laser triangulation [7], time of flight [8], terrestrial laser scanning (TLS) [9], structured light [10], and tomographic methods [11; 12]. The application of UAV laser scanning (UAV-LS) technology in the agricultural field has steadily increased with the recent advancements in UAV technology [13]. In addition, full-waveform LiDAR data can store the entire echo scattered by an illuminated object with different temporal resolutions [14; 15; 16; 17; 18; 19]. In [20], the DJI Zenmuse L1 multi-temporal point cloud was utilized to simulate ULS, and the vertical stratification of various crops was evaluated. Passive techniques, on the other hand, principally revolve around reconstructing 3D point clouds from 2D images collected from various vantage points. Within the realm of passive measurement technologies, the primary theologies and algorithms include the Structure from Motion (SfM) technique [21; 22], the Multi-View Stereo (MVS) technique [23], and space carving [24; 25]. Discerning various attributes associated with plant phenotypes often necessitates an initial and accurate segmentation of the plant structures. Precisely segmenting plant organs is essential for high-throughput and high-precision phenotype analysis. However, due to the intrinsic complexity of plant structures and the morphological variability that occurs throughout the plant life cycle, achieving precise segmentation is challenging. Over the last decade, numerous scientists have utilized machine learning algorithms and constructed an array of models to intensively research plant organ segmentation. Standard practice involves pre-computing the local features of plant point clouds, including surface normal, curvature, smoothness [26; 27; 28], local covariance matrices [29], tensor [30], and point feature histograms [31; 32; 33]. The foundation of these methods lies in the noticeable heterogeneity in the morphological structures of different plant organs.