<p>Instance-level segmentation of large-scale 3D point clouds is a fundamental yet challenging task due to complex spatial structures, severe occlusions, and long-range dependencies. These challenges are particularly pronounced in field-scale agricultural scenes, where dense planting, irregular point distributions, and repetitive plant geometries limit the effectiveness of existing point cloud segmentation methods. This paper proposes HiMamba-Net, a Hilbert-serialized Mamba-based framework for efficient and spatially coherent instance segmentation of agricultural point clouds. The core contribution lies in a spatial serialization strategy that leverages Hilbert space-filling curves to preserve geometric locality while enabling linear-complexity sequential modeling via selective state space models. HiMamba-Net integrates patch-based local feature extraction, multi-scale graph context aggregation, and spatially ordered sequence modeling through SAMamba blocks to capture both local structures and global dependencies effectively. A multi-task learning scheme jointly optimizes semantic segmentation, discriminative instance embeddings, and offset regression to achieve accurate instance separation. Experiments on the Crops3D dataset demonstrate that HiMamba-Net outperforms the evaluated baseline methods, achieving 91.8% mIoU and 91.9% mAP for semantic and instance segmentation, respectively. Ablation studies further validate the contribution of Hilbert serialization and selective state space modeling. These results indicate that spatially coherent serialization combined with linear-complexity sequence modeling provides an effective approach for large-scale 3D point cloud instance segmentation.</p>