3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine-scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce (Picea abies) and pine (Pinus sylvestris) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1-score, mask intersection-over-union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1-score of 86.1%. Pine seedlings yielded higher recall and F1-scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi-automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.