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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.
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A sweet pepper dataset which was captured in the CKA glasshouse using PathoBot. It contains differently coloured sweet peppers in various ripening stages. More information, citations and a related previous dataset can be found at http://agrobotics.uni-bonn.de/sweet_pepper_dataset/
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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).
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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.
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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 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.
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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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µCt images with a resolution of 11µm
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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 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.