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Part 1: Document Description
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Citation |
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Title: |
MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring |
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Identification Number: |
doi:10.60507/FK2/OX9XTM |
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Distributor: |
bonndata |
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Date of Distribution: |
2025-07-16 |
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Version: |
1 |
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Bibliographic Citation: |
Chong, Yue Linn, 2025, "MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring", https://doi.org/10.60507/FK2/OX9XTM, bonndata, V1 |
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Citation |
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Title: |
MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring |
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Identification Number: |
doi:10.60507/FK2/OX9XTM |
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Authoring Entity: |
Chong, Yue Linn (University of Bonn) |
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Grant Number: |
EXC-2070 - 390732324 – PhenoRob |
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Distributor: |
bonndata |
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Access Authority: |
Chong, Yue |
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Depositor: |
Chong, Yue Linn |
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Date of Deposit: |
2025-03-10 |
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Holdings Information: |
https://doi.org/10.60507/FK2/OX9XTM |
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Study Scope |
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Keywords: |
Agricultural Sciences, Computer and Information Science, Other |
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Abstract: |
Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Materials |
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A developer's kit for users to process our data is located in our MuST-C code repository on GitHub: https://github.com/PRBonn/MuST-C The full list of the required libraries is listed on our MuST-C GitHub repository as well: https://github.com/PRBonn/MuST-C/blob/main/requirements.txt |
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Related Publications |
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Citation |
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Title: |
Y. L. Chong, J. Krämer, E. Chakhvashvili, E. Marks, F. Esser, A. Dreier, R. A. Rosu, K. Warstat, R. Pude, S. Behnke, O. Muller, U. Rascher, H. Kuhlmann, C. Stachniss, J. Behley, L. Klingbeil. The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring, (under review) (2025). |
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Bibliographic Citation: |
Y. L. Chong, J. Krämer, E. Chakhvashvili, E. Marks, F. Esser, A. Dreier, R. A. Rosu, K. Warstat, R. Pude, S. Behnke, O. Muller, U. Rascher, H. Kuhlmann, C. Stachniss, J. Behley, L. Klingbeil. The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring, (under review) (2025). |
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