Spectral and Three-Dimensional High-Throughput Phenotypes As Indicators of Plant Variability in a Maize Breeding Program.

Variability is a key factor in a plant breeding program. It is a prerequisite for genetic improvement and also valuable in studies of environmental effects in genotypic expression. However, taking phenotypic measurements of variability in the field is always a laborious and time-consuming task. Through the last decades, remote sensing techniques have been applied to crop sciences in order to decrease time consumption and labor-work and improve precision in crop monitoring. Lately, with the advent of high-resolution sensors and unmanned aerial systems, remote sensing is proving to be an efficient tool for high-throughput field phenotyping (HTFP). In this context, the present research aims to compare two different metrics issued from HTFP techniques in the scope of their ability to express the variability within and among different hybrid maize (Zea mays L.) populations. As vegetation indices are well-known and widely used, the interest of this study was to compare the normalized green red difference index (NGRDI) with the plant heights issued from a recent point clouding technique structure from motion (SfM). 250 hybrid maize genotypes in three treatments (optimal dryland, optimal irrigated, late planted heat stress) with two replications, belonging to the Genome to Fields (G2F) GxE project were imaged in College Station, TX, weekly through the growing season and bi-weekly during the flowering period. An Unmanned Aerial System (UAS) composed by a Tuffwing fixed-wing drone and a Sony high resolution RGB camera was used in the acquisition of the aerial images. With the aid of Agisoft PhotoScan software, the images were processed into orthomosaics, point clouds and digital surface models (DSM) that allowed the extraction of the NGRDI and plant heights. An evaluation was made to determine whether the variability of different maize hybrids can be better perceived by the use of a three-dimensional variable and lead to more accurate decisions in a corn breeding program.