## 2 Materials and Methods
### The Datasets  
As is well known, dataset creation is vital for deep learning algorithms. The creation of high-quality annotated datasets is labor-intensive and requires skilled annotation efforts. At the individual plant level, there is still a shortage of point cloud datasets clipped from plots, thus limiting the application of deep learning to such tasks. Although plant point cloud datasets at the organ level are relatively more abundant, they face several challenges. Specifically, semantic annotation of the plant point cloud at the organ level is also hindered  
Figure 2: UAV data acquisition. (**a**) Data collection site. (**b**) UAV collection. (**c**) Coverage area and flight path for the maize plot. (**d**) DJI Matrice 350 RTK. (**e**) DJI Zenmuse L2. (**f**) SHAR 100M PRO. (**g**) Five-direction flight route. by the complex morphological structure of the plants. Despite the introduction of tools designed to simplify this process, like Label3 DMaize [84], they are typically specialized for certain plant species, making them unsuitable for varied crops with diverse morphological characteristics. As plants mature, their morphological structures become more complex, which contributes to the intricacy of the annotation process. While there has been a considerable upward trend in the generation of 3D plant datasets in recent years, the availability of suitable datasets for plant organ segmentation tasks is relatively limited. We acknowledge several notable datasets that excel in ultra-high spatial resolution and comprehensive annotations, including Plant3D [85], ROSE-X [11], Pheno4D [86], Soybean-MVS [87], and LAST-Straw [88], each of which is detailed in Table 1. However, these datasets were procured from controlled environments with simplistic backdrops, contain a limited variety of crops, and the provision of labeled datasets is still sparse. A significant deficit persists for high-quality datasets derived from natural planting environments. In comparison, the data annotation work in this study addresses some of these shortfalls to an extent. We have conducted outdoor data collection in field and potted environments, accurately depicting real-life planting scenarios. Furthermore, we have acquired diverse plant point cloud data utilizing both active methods, including TLS and UAV-LS, and passive methods, which involve image-generated point clouds. The validation of data collected from different platforms can verify the potential of existing deep learning models in agricultural applications. Figure 3 presents a subset of our data. During the data annotation process, it was observed that the maize point cloud data collected by UAV-LS was relatively sparse, as illustrated in Figure 3d,e, making it challenging to distinguish partly morphological structures of maize. Consequently, it was deemed that such data would be inadequate for organ-level segmentation. UAV-LS technology has been widely applied in field crop scenarios, typically to extract phenotypic parameters of individual plants. Therefore, an attempt is being made to segment these point clouds into individual plants. The point cloud derived from UAV RGB images (Figure 3c) was utilized for the organ segmentation task. Moreover, to augment the point cloud data acquired from various sensors, we utilized point cloud datasets of climbing-stage potato plants [89] and fruit-bearing tomato plants [90] provided by Huazhong Agricultural University's phenotyping platform. These datasets were collected from greenhouse environments using the FARO FocusS70 scanner and the EOS 77D single-lens reflex camera, respectively. Notably, a Multi-View Stereo (MVS) algorithm was employed for the 3D reconstruction of the tomato plants. Table 1 presents the aforementioned point cloud datasets alongside the point cloud datasets generated in this study.