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  • This data set contains online survey data from an experiment investigating German public attitudes towards agricultural robots. Major components are the data set containing 2,269 complete questionnaires (after data cleaning), the according Stata do-file to analyze the data and the output files from the preceding construct validity tests in SPSS.

  • µCt images with a resolution of 11µm

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

  • The corn Dataset (CN20) was captured using BonnBot-I. This is a challenging dataset for crop monitoring approaches as it is a grass crop.

  • This data set is generated by the bio-economic farm-level model FarmDyn (https://farmdyn.github.io/documentation/). X_raw.parquet.gzip contains the input data (77 variables), and Y_raw.parquet.gzip contains the corresponding output data (248 output variables). A farm in FarmDyn maximizes its profit based on the what input values are given (e.g. crop prices, farm size, etc.). The output variables are either a farm's decisions of farming activites or the outcomes of its decisions. The data can be read in python by pd.read_parquet.

  • This dataset contains point clouds of sugar beet plants in field conditions. The data was recorded at Bundessortenamt and was manually labeled for leaf instance segmentation. If you want to use the dataset please contact me.

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    This dataset contains the multitemporal RGB-Image field patches of the "PhenoRob Core Project 5 Mixed Cropping" experiment located at Campus Klein-Altendorf from 2020. 320 Field Patches, including both bean-wheat mixtures but also reference monocultures, were overflown by drone at 11 different time points (RGB) during the growing season. The cropped orthomosaics were rotated for ease of handling and processed to a uniform ground resolution of 3 mm. File endings 'A' and 'B' stand for two different used cameras (also different drones), resulting in slight spectral differences in the images. However, all RGB images are in TIFF format and of type UINT8.

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    Data to replicate findings of Schulz, D., & Börner, J. (2022). Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: A meta‐analysis. Journal of Agricultural Economics, 1477-9552.12521.

  • 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.