MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring

Citation Metadata

Title title

MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring

Alternative URL alternativeURL

https://www.ipb.uni-bonn.de/data/MuST-C/

Author author

1. Author Name

   Chong, Yue Linn

   Author Affiliation

   University of Bonn

   Author Identifier Scheme

   ORCID

   Author Identifier

   Type Name

   authorIdentifier

   Multiple

   False

   Type Class

   primitive

   Value

   https://orcid.org/0000-0002-5851-953X

   Expandedvalue

   Person Name

   Chong, Yue Linn

   @id

   https://orcid.org/0000-0002-5851-953X

   Scheme

   ORCID

   @type

   https://schema.org/Person

Dataset Contact datasetContact

1. Dataset Contact Name

   Chong, Yue

   Dataset Contact Affiliation

   University of Bonn

   Dataset Contact Email

   ychong@uni-bonn.de

Ds Description dsDescription

1. Ds Description Value

   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.

   Ds Description Date

   2025-07-15

Subject subject

1. Agricultural Sciences
2. Computer and Information Science
3. Other

Subject Refinement subjectRefinement

1. Subject Refinement Value

   Geophysics and Geodesy: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography [315-02]

2. Subject Refinement Value

   Agriculture, Forestry and Veterinary Medicine [207]

3. Subject Refinement Value

   Computer Science: Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation [409-05]

Free Keyword freeKeyword

1. Free Keyword Value

   phenotypic trait estimation

2. Free Keyword Value

   LAI

3. Free Keyword Value

   biomass estimation

4. Free Keyword Value

   sugar beet

5. Free Keyword Value

   maize

6. Free Keyword Value

   wheat

7. Free Keyword Value

   potato

8. Free Keyword Value

   soybean

9. Free Keyword Value

   intercrops

10. Free Keyword Value

    RGB

11. Free Keyword Value

    multispectral

12. Free Keyword Value

    lidar

Publication publication

1. Publication Relation Type

   IsSupplementTo

   Publication 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. Sci Data (2026). https://doi.org/10.1038/s41597-025-06462-y

   Publication IDType

   doi

   Publication IDNumber

   10.1038/s41597-025-06462-y

   Publication URL

   https://doi.org/10.1038/s41597-025-06462-y

Language language

1. English

Grant Number grantNumber

1. Grant Number Agency

   German Research Foundation under Germany’s Excellence Strategy

   Grant Number Value

   EXC-2070 - 390732324 – PhenoRob

Depositor depositor

Chong, Yue Linn

Date Of Deposit dateOfDeposit

2025-03-10

Related Material relatedMaterial

1. 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