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  • The global supply of phosphorus is decreasing. At the same time, climate change reduces the water availability in most regions of the world. Insights on how decreasing phosphorus availability influences plant architecture is crucial to understand its influence on plant functional properties, such as the root system’s water uptake capacity. In this study we investigated the structural and functional responses of \textit{Zea mays} to varying phosphorus fertilization levels focusing especially on the root system’s conductance. A rhizotron experiment with soils ranging from severe phosphorus deficiency to sufficiency was conducted. We measured architectural parameters of the whole plant and combined them with root hydraulic properties to simulate time-dependent root system conductance of growing plants under different phosphorus levels. We observed changes of the root system architecture, characterized by decreasing crown root elongation and reduced axial root radii with declining phosphorus availability. Modeling revealed that only plants with optimal phosphorus availability sustained a high root system conductance, while all other phosphorus levels led to a significantly lower root system conductance, both under light and severe phosphorus deficiency. We postulate that phosphorus deficiency initially enhances root system function for drought mitigation but eventually reduce biomass and impairs root development and water uptake in prolonged or severe cases of drought. Our results also highlight the fact that root system organization, rather than its total size, is critical to estimate important root functions.

  • 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.

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

  • 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.

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

  • 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.

  • 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 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.

  • 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.

  • 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