Pronounced heterogeneity in fruit production among macaw palms (Acrocomia aculeata) poses significant challenges for optimizing management and scaling commercial plantations in Brazil. This study integrates high-density UAV-borne LiDAR with deep learning to enable automated, large-scale assessment of fruiting status in macaw palm. LiDAR data with point cloud densities ranging from 653 to 1625 points m⁻² were collected across four commercial stands in southeastern Brazil. Field surveys georeferenced fruiting and non-fruiting palms, enabling segmentation of individual point clouds that were converted into two-dimensional height-colored images for model training and validation. Three convolutional neural network (CNN) architectures (ResNet-18, VGG-16, and DenseNet-121) were evaluated, with ResNet-18 achieving the highest classification accuracy (75%) and most stable training convergence. YOLOv8 was applied to canopy height models for automated palm detection and integrated with ResNet-18 to generate plantation-scale maps of fruiting status. YOLOv8 achieved a 92.5% F1-score, with reduced performance for shorter palms (<4.8 m). Classification performance was strongly dependent on point cloud density: high-density data (>1173 points m⁻²) showed higher accuracy for fruiting palms but increased misclassification of non-fruiting palms, while low-density data (653 points m⁻²) exhibited the opposite bias. The complete workflow achieved 64.6% overall accuracy. These results demonstrate the potential of UAV-borne LiDAR and deep learning for precision management of macaw palm plantations and highlight the need to standardize LiDAR acquisition parameters, particularly point cloud density, before operational deployment.