Abstract: Phenotypic extraction is often required for the facility-cultivated cucumber plants. The major challenges can arise from their significant morphological transformations across growth stages, frequent occlusions from the overlapping leaves and stems, greenhouse lighting, and variable suboptimal solutions. Particularly, the traditional manual phenotyping is labor-intensive, time-consuming, and subjective. It is seriously limited to the large-scale, high-throughput plant breeding and precision agriculture. In this study, a fully automated, robust, and highly accurate framework was developed and validated to quantify the key parameters of the cucumber growth. A multi-modal data acquisition was also constructed to capture the synchronized RGB and depth images. Both top-down and lateral perspectives were also selected to record the data. Complete spatial coverage of the cucumber canopy was obtained over its entire life cycle. A lightweight instance segmentation model (named YOLO-LCOS) was also developed, according to an enhanced YOLOv11n-seg architecture. The HGNetV2 network was integrated as the backbone for more computationally efficient feature extraction. A Hierarchical Scale-wise Feature Pyramid Network (HSFPN) module was incorporated into the network neck for superior multi-scale feature fusion. The plant organs of vastly different sizes were effectively segmented after enhancement. An Efficient Multiscale Attention (EMA) mechanism was embedded to adaptively prioritize salient features while suppressing the irrelevant background clutter and noise. An extraction pipeline for the phenotypic parameter was engineered after segmentation. The high-precision mask outputs from YOLO-LCOS were fused with 3D spatial information from the depth maps. Furthermore, geometric modeling techniques were then applied to quantitatively measure the critical agronomic traits, including the total leaf count, total and individual leaf surface area, plant height, and flower count. Experimental results show that the highly positive superior performance was achieved in the refined YOLO-LCOS model after improvement, compared with the baseline YOLOv11n-seg. There were the 7.8% and 8.8% reductions in the floating-point operations and the total parameters, respectively, indicating a more streamlined architecture. Remarkably, this complexity reduction coincided with a 1.5% increase in the inference speed. More critically, the segmentation accuracy was improved substantially: The mean Average Precision (mAP), precision, and recall increased by 8.8 percentage points, 7.1 percentage points, and 6.9 percentage points, respectively. The end-to-end phenotypic pipeline was evaluated after optimization. The results revealed that there was an exceptionally strong agreement with manual ground-truth data. The coefficients of determination (R²) were 0.95 for the leaf count, 0.96 for leaf area, 0.95 for plant height, and 0.90 for flower count, respectively. The low error metrics were achieved in: Root Mean Square Errors (RMSE) were 0.86 for leaf count, 8.20 cm² for leaf area, 9.80 cm for plant height, and 0.81 for flower count, respectively; Mean Absolute Errors (MAE) were 0.50, 6.50 cm², 7.21 cm, and 0.54, respectively. Particularly, the high accuracy was observed for the complex traits, like the leaf area and plant height, indicating the pipeline's resilience to occlusions and precise spatial reasoning. In conclusion, an integrated framework was successfully developed and validated to accurately extract the key phenotypic parameters from the cucumber plants under complex facility environments. Notably, the lightweight YOLO-LCOS segmentation model was integrated with its HGNetV2 backbone, HSFPN module, and EMA mechanism. The highly effective performance was achieved in the morphological variation, occlusion, and lighting interference. Consequently, a powerful and reliable pipeline for the trait quantification was formed with the advanced image segmentation, spatial depth information, and geometric modeling. The accurate, robust, efficient, and practical approach can be expected to deploy for the real-world greenhouse. Thus, this finding can provide a solid technical foundation for the continuous, non-invasive, high-throughput digital crop monitoring. Significant promise can also be held to advance precision horticulture. Critical data can be supplied for the optimal cultivation, intervention, and breeding programs for facility cucumbers. A reliable technological solution can also be supported for data-driven decision-making for the digital monitoring and fine-grained production in the greenhouse.