Integrating UAV-based high-throughput phenotyping and GWAS reveals favorable alleles for low nitrogen adaptation in wheat

1.IntroductionThis repository contains the dataset associated with the study“ Integrating UAV-based high-throughput phenotyping and GWAS reveals favorable alleles for low nitrogen adaptation in wheat”. The dataset comprises manually measured yield and biomass data collected at harvest for 210 wheat varieties. It also includes UAV-acquired multispectral imagery captured across five distinct growth stages, alongside 22 vegetation indices extracted from these images. The multispectral imagery was captured using an EcoDrone UAS-8 multifunctional unmanned aerial vehicle (Beijing EcoTech Science and Technology Ltd., Beijing, China) equipped with a RedEdge-MX Multispectral camera (MicaSense, USA). Subsequently, QGIS and ENVI software were utilized to delineate individual plots and extract the vegetation indices. 2.Description of filesThe dataset is structured into three primary folders:● Yield_and_Biomass: Contains two Excel spreadsheets documenting the manual yield and biomass measurements for the 2022 and 2023 growing seasons. The dataset evaluates 210 wheat varieties under two nitrogen fertilization treatments: N0 (0 kg ha⁻¹) and N240 (240 kg ha⁻¹). ● UAV_Multispectral_Images: Comprises compressed archives of multispectral imagery from both years. Each archive contains images captured across five distinct growth stages (Z24, Z31, Z43, Z75, and Z83). Note that these images have been pre-cropped based on QGIS-generated buffers. ● Vegetation_indices: Provides 22 extracted vegetation indices for the respective years and growth stages in CSV format. Within these files, identical IDs represent biological replicates, encompassing a total of 1,260 experimental plots per dataset.