PhenoRoam
dataset
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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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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.
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Sugar beet shoot and root phenotypic plasticity to nitrogen, phosphorus, potassium and lime omission
Data were collected in 2019 in Dikopshof, Wesseling, Germany
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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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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.
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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.
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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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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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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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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.