Full description
The dataset provides a comprehensive collection of UAV (Unmanned Aerial Vehicle) flights conducted over several years, specifically focusing on a wheat experiment. The flights encompass two sensor configurations: Multispec and RGB. The Multispec flights were captured using the Phantom 4 M in 2020 and the MicaSense Altum in 2021 and 2022. The RGB flights were captured using the Phantom 4 Pro in 2020 and the Zenmuse P1 in 2021 and 2022. Each flight in the dataset is associated with a specific set of dates within each year, offering temporal coverage for the wheat experiments, enabling time-series analysis.The dataset centres around experimental plots within wheat trials in Gatton and Boorowa, serving as specific features of interest. Shapefiles are included to extract multispectral and RGB vegetation indices as well as geometric traits from within these plots. These shapefiles enable precise delineation of the experimental plots and facilitate the extraction of traits. Ground-truth measurements were made at the same time as UAV flights at key growth stages, and parameters including dry biomass and fresh biomass (1647 observations), along with Leaf Area Index, phenology, plant emergence, head number and stem number were recorded.This dataset includes Agisoft Metashape .psx files for each flight, processed orthomosaics, digital elevation models and point clouds for 46 dates across 2020, 2021 and 2022 in 2 locations (Gatton and Boorowa). The combination of Multispec and RGB sensor configurations, along with the utilization of shapefiles, orthomosaics, and DEMs, provides researchers with comprehensive data for analysing and understanding the relationship between vegetation indices, geometric traits, and ground-truth data within the experimental plots of the wheat trials. This dataset is particularly valuable for agricultural research, allowing for in-depth analysis of wheat growth patterns, crop health, and geometric attributes within controlled experimental settings.Issued: 2023
Data time period:
Data collected from: 2020-01-01T00:00:00Z
Data collected to: 2022-01-01T00:00:00Z
Subjects
Agricultural, Veterinary and Food Sciences |
Crop and Pasture Improvement (Incl. Selection and Breeding) |
Crop and Pasture Production |
Field-Based |
Ground-truth |
Multispectral |
RGB |
UAV |
Wheat |
eng |
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Other Information
Scaling up high-throughput phenotyping for abiotic stress selection in the field
local : UQ:d04d5e8
Smith, Daniel T., Potgieter, Andries B. and Chapman, Scott C. (2021). Scaling up high-throughput phenotyping for abiotic stress selection in the field. Theoretical and Applied Genetics, 134 (6), 1845-1866. doi: 10.1007/s00122-021-03864-5
Research Data Collections
local : UQ:289097
GRDC Data Collections
local : UQ:06510ce
Identifiers
- Local : RDM ID: 9126c4f0-fb61-11ed-bbd1-c96e470a9b18
- DOI : 10.48610/951F13C