From 1 - 10 / 20
  • 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

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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.

  • 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.

  • This data set is generated by the bio-economic farm-level model FarmDyn ( 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.

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

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    This dataset contains semantic segmentation train-validation-test data splits of (1) the ISPRS Potsdam orthomosaic (, (2) the RIT-18 landcover orthomosaic (, and (3) the industrial environment of the photorealistic Flightmare quadrotor simulator ( 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 ( 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.

  • 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 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 dataset contains georeferenced, image-based UAV time-series of two monitored cauliflower fields acquired in two years, 2020 and 2021. The proposed dataset contains RGB and multispectral orthophotos with coordinates of approximately 14000 individual cauliflower plants. The coordinates enable extraction of complete and incomplete time-series of image patches showing individual plants. The dataset contains the collected phenotypic traits of 740 plants, including the developmental stage and plant and cauliflower size. The harvestable product is completely covered by leaves, thus, plant IDs and coordinates are provided to extract image pairs of plants pre- and post-defoliation. In addition, to facilitate classification, detection, segmentation, instance segmentation, and other similar computer vision tasks, the proposed dataset contains pixel-accurate leaf and plant instance segmentations, as well as stem annotations.

  • Processed data to replicate the working paper "No impact of SMS-based information provision on knowledge and practice adoption among smallholder peanut farmers".