PhenoRoam
dataset
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Reflectance orthomosaics of PhenoRob Central Experiment derived from 2021 multispectral image data.
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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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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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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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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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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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µCt images with a resolution of 11µm
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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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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.