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  • Data represent an intercropping field experiment carried out at CKA in the year 2021. Shoot and root data collection was conducted with one faba bean cultivar and two spring wheat cultivars sown at three sowing densities. FTIR spectroscopy was used to define the root masses of the two species.

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    The dataset RGB-MiniplotBarley contains 22164 RGB images of 11 time points 21. jun - 7. jul 2022 of two genotypes of spring barley grown under single-nutrient deficiencies of N, P, K, S, B and one multi-nutrient deficient treatment from miniplot fertilizer trial at Campus Klein-Altendorf of University of Bonn. The images are annotated with plot number, genotype and fertilizer management and have been taken for a Deep Learning approach for nutrient deficiency recognition.

  • Shape completion dataset for sweet peppers. A detailed description of the dataset can be found in our technical report: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2024arxiv.pdf We additionally provide a small set of python script to load this data: https://github.com/PRBonn/shape_completion_toolkit

  • The dataset contains 7 maize plants measured on 12 days. This gives 84 maize point clouds (about 90 Mio. points). From these, 49 point clouds (about 60 Mio. points) are labeled. Furthermore, the dataset contains 7 tomato plants measured on 20 days (about 350 Mio. points). This gives 140 point clouds from which 77 point clouds (200 Mio. points) are labeled. Note that we provide temporally consistent labels for each point in the clouds. We provide labeled and unlabeled point clouds, the file name indicates whether the point cloud is annotated or not. For example, M01_0313_a.xyz is labeled, M01_0314.xyz is not labeled. For the tomato plant point clouds, each annotated file contains the x,y,z coordinates, and the label associated with the point. For the maize point clouds. Each file annotated contains the x,y,z coordinates, and the 2 labels associated with the point. For both species, if no labels are provided, the files contain only the x,y,z coordinates. Cite: D. Schunck, F. Magistri, R. A. Rosu, A. Cornelißen, N. Chebrolu, S. Paulus, J. Léon, S. Behnke, C. Stachniss, H. Kuhlmann, and L. Klingbeil, “Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis,” PLOS ONE, vol. 16, iss. 8, pp. 1-18, 2021. doi:10.1371/journal.pone.0256340.

  • Reflectance orthomosaics of PhenoRob Central Experiment derived from 2021 multispectral image data.

  • The effect of a mucilage analogue from chia seeds without intrinsic respiratory activity on oxygen diffusion was measured at different water contents during wetting-drying cycles in a diffusion chamber experiment. Artificial soils of various particle size distributions were mixed with various amounts of the mucilage analogue. Additionally, environmental scanning electron microscopy (ESEM) was used to visualize mucilage bridges in the dry soil samples.

  • A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain. Please find more information at: https://www.phenobench.org/

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    This dataset contains semantic segmentation train-validation-test data splits of (1) the ISPRS Potsdam orthomosaic (https://www.isprs.org/education/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx), (2) the RIT-18 landcover orthomosaic (https://github.com/rmkemker/RIT-18), and (3) the industrial environment of the photorealistic Flightmare quadrotor simulator (https://github.com/uzh-rpg/flightmare). All splits are generated by simulating UAV missions at fixed altitudes. We use these datasets in our "An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping" paper. Further, it contains (4) model checkpoints of our proposed Bayesian ERFNet framework (https://github.com/dmar-bonn/bayesian_erfnet) pre-trained on Cityscapes, (5) the ISPRS Potsdam and RIT-18 RGB and semantically labelled orthomosaics, and (6) the Flightmare render binary for the industrial environment.