# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 1 Introduction  
Recognized for its high nutritional content, rapid growth cycle, and strong environmental adaptability, the tomato has become a crop of significant economic value, contributing substantially to both the fresh produce market and the food processing industry [1]. Beyond its agricultural value, the tomato serves as a model plant in genetics, molecular biology, and physiological ecology, providing a crucial foundation for research on modern agricultural technologies, genetic improvement, and precision breeding [2, 3]. Accurate acquisition of organ-level phenotypic traits, such as leaf area and plant height, is essentialfor monitoring growth status, evaluating environmental responses, and supporting breeding decisions. Traditional phenotyping techniques mostly rely on manual measurements, which are time-consuming, damaging, labor-intensive, and inappropriate for dynamic, large-scale, non-invasive applications [4]. With the advancement of precision agriculture, the demand for high-throughput, automated, and non-destructive phenotyping technologies is increasing, making automated plant phenotyping a research hotspot. Early approaches primarily utilized 2D image-based visual perception, traditional learning algorithms, and neural network techniques [5; 6; 7]. However, 2D imaging suffers from limitations such as occlusion and a lack of in-depth information, making it inadequate for capturing the complex 3D structures of plants, especially in cases of overlapping leaves and variable postures, such as in tomato plants. To address these challenges, 3D vision technologies have been introduced into plant phenotyping. 3D plant modeling provides rich spatial structure information suitable for non-invasive growth monitoring and structural analysis. For example, [PERSON] et al. [8] proposed a method combining skeleton extraction and stem-leaf classification for automatic segmentation of maize seedling point clouds. [PERSON] et al. [9] provided a 3D point cloud-based approach combining RANSAC, region expansion, and greedy projection meshing algorithms to precisely estimate phenotypic traits such as leaf area in tomato seedlings. [PERSON] et al. [10] introduced a non-destructive image-based measurement system for automatic detection and size estimation of tomato leaves, combining 2D and 3D data from a Zivid 3D camera to enable 3D monitoring during plant growth. With the rapid development of deep learning in 3D vision, point cloud-based neural networks such as PointNet, PointNet++, and DGCNN have been utilized for segmenting plant point clouds. As a scenario, [PERSON] et al. [11] recommended a phenotyping framework that integrates 3D plant reconstruction employing a lightweight point cloud segmentation network (PointSegNet) in conjunction with the Nerfacto neural radiance field model, which combines global-local set abstraction and edge-aware feature propagation to achieve precise partitioning of stem and leaf structures, as well as robust retrieval of phenotypic features. [PERSON] et al. [12] introduced Win-Former, a window-based Transformer for efficient semantic segmentation of maize point clouds, achieving 83.45% mIoU on the Pheno4D dataset. [PERSON] et al. [13] presented a framework based on an improved Red-Blue Magpie Optimization (ES-RBMO) strategy and a four-layer convolutional model for semantic classification of tomato stems and leaves. [PERSON] et al. [14] designed PEPNet, a 3D deep learning network for extracting traits and segmenting plant organs, significantly improving inference speed and throughput in cotton stem-leaf segmentation. [PERSON] et al. [15] trained PointNet++ and HAIS on augmented datasets for deformable point cloud-based segmentation of maize stems and leaves. [PERSON] et al. [16] proposed a point cloud-based approach for cotton phenotyping using the PointSegAt network and an active boundary segmentation algorithm. [PERSON] et al.