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

Abstract

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

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations

“…So far, several attempts have been successfully applied for using temporal phenotype (e.g. temporal plant height and NDVI) in maize (Han et al ., 2019; Wang et al ., 2019, 2021; Anderson et al ., 2020; Tirado et al ., 2020; Adak et al ., 2021b, 2023b; Oehme et al ., 2022; Rodene et al ., 2022; Chatterjee et al ., 2023), sorghum (Miao et al ., 2020), cotton (Pauli et al ., 2016), and rice (Sun et al ., 2022). However, several important limitations exist when examining temporal phenotypes and incorporating these into QTL or genomic association mapping.…”

“…Field-based high-throughput phenotyping (FHTP) combines cutting-edge remote sensing technologies with traditional breeding and genetics approaches to rapidly and non-invasively assess and quantify a wide range of plant traits in natural field conditions (Araus & Cairns, 2014). Field-based high-throughput phenotyping has gained significant attention and prominence in recent years due to its potential to accelerate crop improvement and enhance our understanding of plant responses to various environmental stresses (Pauli et al, 2016;Shi et al, 2016;Araus et al, 2018;Wang et al, 2019Wang et al, , 2021Zhou et al, 2021;Adak et al, 2021aAdak et al, ,b, 2023bOehme et al, 2022;Sun et al, 2022;Herr et al, 2023;Li et al, 2023). Field-based high-throughput phenotyping involves the integration of advanced sensing technologies, robotics, data analytics, and remote sensing with traditional agronomic practices.…”

“…Temporal data have also proved to be better or complementary datasets for genomic data in prediction of complex traits (Rutkoski et al, 2016;Sun et al, 2019;Adak et al, 2022bAdak et al, , 2023b. Finally, temporal VI values can serve as phenotypic data for genetic studies such as quantitative trait locus (QTL) mapping (Sun et al, 2022) and genome wide association studies (GWAS; Pauli et al, 2016;Adak et al, 2021bAdak et al, , 2023bWang et al, 2021;Rodene et al, 2022;Li et al, 2023). Correlating VI data with genomic information to identify regions of the genome associated with desirable growth traits.…”

What can aerial phenotyping do and bring to us (breeders)?

“…Nevertheless, the present bottleneck for aerial phenotyping is analytical solutions, which need to be readily used to extract meaningful and reliable trait information from the obtained images. An open-source platform called AIRMEASURER developed by Sun et al (2022; pp. 1584-1604, published in this issue of New Phytologist, has made efforts to address this problem.…”

“…An open‐source platform called A ir M easurer developed by Sun et al . ( 2022 ; pp. 1584–1604), published in this issue of New Phytologist , has made efforts to address this problem.…”