AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

• DOI: 10.1111/nph.18314
• Publication Date: 2022-07-28T18:40:17Z
• Authors: Gang Sun; Hengyun Lu; Yan Zhao; Jie Zhou; Robert Jackson; Yongchun Wang; Ling‐xiang Xu; Ahong Wang; Joshua Colmer; Eric Ober; Qiang Zhao; Bin Han; Ji Zhou
• Keywords: Trait; Phenotypic trait

Summary Low‐altitude aerial imaging, an approach that can collect large‐scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively remained challenging. Here, we present A ir M easurer , open‐source expandable platform combines automated image analysis, machine learning original algorithms perform trait analysis using 2D/3D imagery acquired by low‐cost UAVs rice ( Oryza sativa ) trials. We applied the study hundreds landraces recombinant inbred lines at two sites, from 2019 2021. range static dynamic traits were quantified, including crop height, canopy coverage, vegetative indices growth rates. After verifying reliability AirMeasurer‐derived traits, identified genetic variants associated with selected growth‐related genome‐wide association quantitative loci mapping. found ‐derived had led reliable loci, some matched published work, others helped us explore new candidate genes. Hence, believe our work demonstrates valuable advances providing high‐quality phenotypic empower mapping for improvement.