Mungbean UAV Prediction Methodology Dataset

This dataset comprises phenotypic measurements collected from a diverse mungbean (Vigna radiata) germplasm panel evaluated across three field experiments conducted in Queensland, Australia, during 2022-2023. The experiments were established at two locations: the University of Queensland Gatton Research Farm and the Pacific Seeds Farm in Allora. The dataset encompasses multiple trait categories including high-throughput phenotyping-derived vegetative indices, plant geometric characteristics, and ground-based agronomic measurements. The agronomic measurements specifically include total aboveground biomass, early vigor, and stomatal conductance, providing a comprehensive phenotypic profile of the germplasm. The experimental design facilitated the development of prediction models using a strategic subset of mungbean genotypes, which were subsequently validated across the broader germplasm panel. This approach demonstrates the potential for efficient phenotypic prediction in large-scale mungbean breeding programs. Data collection and curation were conducted by researchers at the University of Queensland (UQ): Miss Shanice Van Haeften, Dr Millicent Smith, and Professor Lee Hickey.

本数据集收录了2022-2023年间于澳大利亚昆士兰州开展的3项田间试验中，针对多样化绿豆（Vigna radiata）种质群体进行表型测定所得到的表型测量数据。试验分别设于两个试验点：昆士兰大学盖顿研究农场（University of Queensland Gatton Research Farm）与阿拉拉市的太平洋种子农场（Pacific Seeds Farm）。本数据集涵盖多类性状类别，包括由高通量表型（high-throughput phenotyping）技术衍生得到的营养生长指数、植株几何特征，以及地面采集的农艺性状测量值。其中农艺性状测量具体包含地上总生物量、植株早期活力与气孔导度，可为该种质群体提供全面的表型特征图谱。本试验设计通过利用策略性选取的绿豆基因型子集构建预测模型，并随后在更广泛的种质群体中对模型进行验证。该方案展现了在规模化绿豆育种项目中实现高效表型预测的应用潜力。本数据集的数据采集与整理工作由昆士兰大学（UQ）的研究人员完成：Shanice Van Haeften女士、Millicent Smith博士以及Lee Hickey教授。