In crop production systems, weed management is vitally important. But both manual weeding and herbicide-based weed controlling are problematic due to concerns in cost, operator health, emergence of herbicide-resistant weed species, and environment impact. Automated robotic weeding offers a possibility of controlling weeds in a precise fashion, particularly for weeds growing near crops or within crop rows. However, identification and localization of plants have not yet been fully automated. The goal of this reported project is to develop a high-throughput plant recognition and localization algorithm by fusing 2D color and textural data with 3D point cloud data. Plant morphological models were developed and applied for plant recognition against different weed species at different growth stages.