Abstract
"MuST-C is a multi-sensor agricultural dataset for in-field phenotyping, covering six crop species with RGB, LiDAR, and multispectral data to automate large-scale growth monitoring."
Description
Agriculture IoT Innovation
The MuST-C dataset provides high-resolution data collected over an entire growing season using diverse robotic and aerial IoT platforms.
Sensor Array Details
- LiDAR Systems: 3D point clouds for measuring plant height and leaf density.
- Multispectral: Reflectance data for calculating NDVI and plant health.
- RGB Cameras: High-resolution imagery for automated weed detection.
Strategic Importance
Facilitates the development of foundation models for agriculture, allowing for precise temporal analysis and early stress detection in crops.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on University.
Preview on UniversityCite This Dataset
Chong, Y. L., Krämer, J., Chakhvashvili, E., Marks, E., Esser, F., Dreier, A., Rosu, R. A., Warstat, K., Pude, R., Behnke, S., Muller, O., Rascher, U., Kuhlmann, H., Stachniss, C., Behley, J., & Klingbeil, L. (2026). MuST-C: Multi-Sensor Crop Phenotyping Dataset. {Scientific Data. [Dataset]. {Nature Portfolio. https://doi.org/10.1038/s41597-025-06462-y
Source: {Nature Portfolio (2026) · DOI: 10.1038/s41597-025-06462-y
Indexed by IoTDataset.com on Feb 13, 2026
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