10.60507/FK2/HYI2DSEsser, FelixFelixEsser0000-0003-3119-8585University of BonnKlingbeil, LasseLasseKlingbeil0000-0002-1941-150XUniversity of BonnKuhlmann, HeinerHeinerKuhlmann0009-0004-9758-7721University of Bonnbonndata2026Agricultural SciencesEngineeringEsser, FelixFelixEsserUniversity of BonnAndré CornelißenAndréCornelißen2026-03-192026-05-272023-05-16/2023-08-213D Point Clouds74371191748406722571893871297241365203441869666274501337903353667427987701209853text/plainapplication/pdfapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zip1.0CC BY 4.0This dataset contains high-quality 4D point clouds of multiple crop species (bean, wheat, corn, sugar beet, potato, and brassica) acquired using a specialized field phenotyping robot. The platform is equipped with two industrial-grade laser triangulation scanners (micrometer precision) and a centimeter-accurate georeferencing system comprising RTK GNSS and an inertial navigation system (INS), enabling precise multi-temporal registration of 3D point clouds. High quality is defined by sub-millimeter point precision, sub-millimeter spatial resolution, centimeter-level georeferencing accuracy, and high consistency between the two scanners. The dataset is organized into individual crop plots containing single crop rows of each species, scanned with by the field robot. This dataset enables multi-temporal phenotypic trait determination at the single plant-organ scale under field conditions. The key novelty lies in the combination of field-based acquisition with high-quality reconstruction, addressing the quality limitations of existing datasets created in the field.