2 Works

Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems

Steven Anderson, Seth Murray, Lonesome Malambo, Colby Ratcliff, Sorin Popescu, Dale Cope, Anjin Chang, Jinha Jung & J. Thomasson
Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across three environmental stress treatments, using two UAS platforms. The high-altitude (120-m) fixed-wing platform increased the fraction of variation attributed to genetics and had highly repeatable (R > 60%) height estimates, increasing the genetic variance explained (10–40%) over traditional terminal plant height measurement (PHT TRML ∼30%), as well as over the low-altitude rotary-wing UAS platform...

Unoccupied aerial system enabled functional modeling of maize (Zea mays L.) height reveals dynamic expression of loci associated to temporal growth

Steven Anderson, Seth Murray, Yuanyuan Chen, Lonesome Malambo, Anjin Chang, Sorin Popescu, Dale Cope & Jinha Jung
Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function...

Registration Year

  • 2019

Resource Types

  • Dataset


  • Texas A&M University
  • Texas A&M University – Corpus Christi
  • University of Florida