# The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring

## TL;DR

The MuST-C dataset provides multi-sensor field data for six crops to improve automated phenotyping and trait monitoring.

## Contribution

The novel MuST-C dataset combines multi-sensor and multi-temporal field data with reference measurements for crop phenotyping.

## Key findings

- The dataset includes georeferenced data from RGB, LiDAR, and multispectral sensors collected over a growing season.
- Manual reference measurements of leaf area index and biomass are provided for six crop species.
- The dataset supports the development and comparison of automated phenotyping methods across sensors and crops.

## 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.