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

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    This data set contains survey data from a framed field experiment that was conducted with German crop farmers between February and April 2022. Major components of the data set: a set of psychographic/attitudinal items, results from an economic business simulation game, sociodemographic and farm structural variables (n=334 after data cleaning).

  • 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.

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

  • 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

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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.

  • SB20 is a sugar beet dataset that was captured at a field on campus Klein Altendorf of the University of Bonn. The data was captured by mounting an Intel RealSense D435i sensor with a nadir view of the ground on a Sagarobot. It contains RGB-Dimages of sugarbeet and eight different categories of weeds covering a range of growth stages, natural world illumination conditions, and challenging occlusions. The dataset provides multi-class annotations.

  • 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.

  • 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.

  • 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.