Sugar4Dhttps://doi.org/10.60507/FK2/IS8YBZBömer, JonasMarks, EliasIspizua Yamati, Facundo RamónStachniss, CyrillPaulus, StefanMahlein, Anne-Katrinbonndata2026-03-052026-07-06T06:35:15ZThe Sugar4D project introduces a publicly available, high-quality 4D plant phenotyping dataset of sugar beet plants captured with terrestrial LiDAR. It provides densely sampled, temporally consistent point cloud data with detailed, point-wise organ-level annotations that enable tracking of individual leaves across growth stages. By combining annotated 3D data with extracted morphological traits and reference measurements, the dataset aims to reduce the bottleneck of data acquisition and manual annotation, and to support the development, benchmarking, and validation of methods for spatio-temporal plant analysis, including instance segmentation, temporal registration, organ tracking, and growth-related morphological studies.Agricultural SciencesComputer and Information ScienceEnglishBömer, J., Marks, E., Ispizua Yamati, F.R. et al. Spatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet. Precision Agric 27, 50 (2026)., doi, 10.1007/s11119-026-10337-6, https://doi.org/10.1007/s11119-026-10337-62021Bömer, Jonas2026-02-182021-10-262021-12-21Pheno4D - A large scale spatio-temporal dataset of point clouds of maize and tomato plants (https://www.ipb.uni-bonn.de/data/pheno4d/index.html)LAST-Straw - Lincoln’s Annotated Spatio-Temporal Strawberry Dataset (https://lcas.github.io/LAST-Straw/)point cloudsautomatic morphological measurementsmanual reference measurementsCC BY 4.0