This study investigates a weighted ensemble framework for individual tree crown (ITC) delineation using LiDAR-derived canopy height models (CHMs). Three deep learning models, Mask R-CNN, U-Net, and YOLO were first independently evaluated to establish the baseline performance under consistent training and evaluation conditions. A weighted ensemble was then constructed by combining model outputs through a voting‑based fusion scheme, with an exhaustive search performed across multiple weight configurations to identify the ones that maximize common evaluation metrics. While certain weighting configurations yielded improvements in quantitative measures such as intersection over union (IoU), recall, F1 score, and accuracy relative to individual models, qualitative analysis revealed that these gains often coincided with substantial under-segmentation, manifested as large, merged crown regions. This discrepancy highlights the limitations of binary map voting for instance-level delineation and indicates that metric-driven ensemble optimization may not reliably reflect instance-level segmentation quality. The findings suggest that more expressive fusion strategies may be necessary for effective ensemble-based ITC delineation in future work.