Integrating UAV multispectral imaging and proximal sensing for high-precision cereal crop monitoring

The dataset for "Integrating UAV Multispectral Imaging and Proximal Sensing for High-Precision Cereal Crop Monitoring" is structured into multiple folders, encompassing UAV-derived imagery, statistical summaries, Plant-O-Meter data, and ground truth data for comprehensive crop analysis. It includes: Clipped Experimental Field Images - Georeferenced TIFF files representing segmented experimental fields. Summary Statistics – CSV files containing calculated metrics (mean, max, median, and min) across all recorded dates and vegetation indices. Multispectral Data – UAV-based RGBNre (Red, Green, Blue, and Red Edge) imagery collected across all recorded dates, stored in TIFF format. RGB Imagery – Standard UAV-captured RGB TIFF images spanning all acquisition dates. Orthomosaic Images – High-resolution georeferenced maps generated by stitching together multiple UAV images for detailed field visualization. Shapefiles (.SHP) – Spatial boundary and annotation data for the experimental fields, supporting geospatial analyses. Zonal Statistics – Shapefiles containing spatial summaries (mean, median, min, max) of vegetation indices and other field attributes. Additionally, the dataset incorporates Plant-O-Meter data, recorded on four distinct dates, covering vegetation indices that were specifically selected to match those in the UAV dataset, ensuring direct comparability between proximal sensor and UAV measurements. The ground truth data includes genotype codes and names, yield measurements, plant height, and metadata on replications, reseeding, and treatments. This dataset is designed to facilitate high-precision crop monitoring by integrating UAV-based multispectral imaging with proximal sensing data, enabling detailed analysis of cereal crop performance under varying conditions.