Abstract: Accurate extraction of phenotypic parameters from cigar tobacco plants is essential for scientifically assessing plant growth status, guiding precision breeding, and optimizing cultivation management. However, cigar plants exhibit complex structural characteristics, such as severe inter-layer occlusion and tightly overlapping leaves, which often lead to point cloud adhesion and blurred instance boundaries. To address these challenges, this study proposes a novel method for instance segmentation and phenotypic parameter extraction based on 3D point clouds. First, we develop an enhanced 3D point cloud instance segmentation model, termed PnP-3D-PSegNet, which integrates geometric structure with global contextual semantics. Specifically, a PnP-3D module is embedded into the encoder of the baseline PSegNet architecture to improve the model’s ability to capture fine-grained structural features in complex plant point clouds. This design enables more precise discrimination of individual plant organs under challenging conditions. Second, to tackle the issues of blurred instance boundaries and leaf adhesion, an improved Mean Shift clustering algorithm is introduced. We design an instance-aware clustering strategy that combines edge-aware feature integration with density–distance weighted constraints. By incorporating an edge perception mechanism based on local curvature and variations in surface normals, edge confidence is estimated and integrated with instance features, thereby guiding the clustering process in an enhanced feature space and effectively reducing the influence of unreliable boundary points. This significantly alleviates the boundary ambiguity problem in instance segmentation. Furthermore, to mitigate the incorrect assignment of points between adjacent leaves under adhesion conditions, a weighted criterion based on local point density and the distance to cluster centers is employed, further improving segmentation accuracy in densely overlapping regions. Experimental results demonstrate that the proposed PnP-3D-PSegNet model achieves notable improvements over the baseline PSegNet. Specifically, the model reaches 93.59% in mean coverage, 95.77% in mean weighted coverage, 97.21% in mean precision, and 98.55% in mean recall, representing increases of 3.39, 3.56, 3.65, and 2.78 percentage points, respectively. These results highlight the effectiveness of the proposed feature fusion and optimization strategies. Based on the segmentation outputs, six key phenotypic parameters are further extracted, including plant height, leaf number, leaf length, leaf width, leaf area, and canopy width. Comparisons with manual measurements show strong agreement, with coefficients of determination (R²) of 0.96, 0.97, 0.92, 0.95, 0.98, and 0.97, respectively. The corresponding root mean square errors (RMSE) are 1.2 cm for plant height, 0.82 cm for leaf length, 1.13 cm for leaf width, 1.26 cm for canopy width, 11.83 cm² for leaf area, and 0.32 leaves for leaf count. These results confirm the high accuracy and reliability of the proposed method. Additionally, experiments conducted on a tomato point cloud dataset further demonstrate the strong cross-crop generalization capability of PnP-3D-PSegNet. Overall, this study establishes a complete pipeline from point cloud segmentation to phenotypic parameter extraction. The proposed feature fusion and boundary optimization strategies not only effectively address the problem of leaf adhesion in cigar tobacco plants but also provide valuable technical insights for 3D phenotypic analysis of other complex crops.