Data for: Advancing harvest table beet root yield estimation via unmanned aerial systems (UAS) multi-modal sensing

This comprehensive dataset captures the growth and development of table beets (Beta vulgaris) throughout the 2021 and 2022 growing seasons using unmanned aerial system (UAS) technology. The collection represents an agricultural remote sensing resource that combines multiple imaging modalities with ground truth measurements.

Dataset Highlights:
Multi-modal Imagery: Contains high-resolution hyperspectral imagery capturing detailed spectral signatures across numerous wavelengths, alongside five band multispectral (RGB + Red-edge + NIR) imagery of the same plots, allowing for comparative analysis between different sensors.

Structural Data: Features canopy height models derived from both structure-from-motion photogrammetry and LiDAR technology, providing precise measurements of crop canopy development over time.

Temporal Coverage: Multiple collection dates throughout the growing seasons enable tracking of crop development from early growth stages through harvest.

Ground Truth Measurements: Comprehensive field measurements including root yield, root number, plant density, foliage dry weight, and root diameter provide excellent validation data for remote sensing models.

Environmental Context: Includes detailed meteorological data throughout both growing seasons, allowing researchers to account for weather effects on crop development.

Experimental Design: The 2022 data includes three distinct plot locations with different treatments, offering opportunities for comparative analysis across different growing conditions.

This dataset serves as an invaluable resource for researchers and practitioners in precision agriculture, remote sensing, machine learning, and crop science. It can support the development of yield prediction models, crop monitoring applications, and the advancement of UAV-based agricultural sensing techniques.