A 3D time‐of‐flight camera was applied to develop a crop plant recognition system for broccoli and green bean plants under weedy conditions. The developed system overcame the previously unsolved problems caused by occluded canopy and illumination variation. An efficient noise filter was developed to remove the sparse noise points in 3D point cloud space. Both 2D and 3D features including the gradient of amplitude and depth image, surface curvature, amplitude percentile index, normal direction, and neighbor point count in 3D space were extracted and found effective for recognizing these two types of plants. Separate segmentation algorithms were developed for each of the broccoli and green bean plant in accordance with their 3D geometry and 2D amplitude characteristics. Under the experimental condition where the crops were heavily infested by various types of weed plants, detection rates over 88.3% and 91.2% were achieved for broccoli and green bean plant leaves, respectively. Additionally, the crop plants were segmented out with nearly complete shape. Moreover, the algorithms were computationally optimized, resulting in an image processing speed of over 30 frames per second.