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

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 (10–20%). A logistic function
reduced the dimensionality (>20 flights) of each UAS dataset to
three parameters (inflection point, growth rate, and asymptote) and
produced a more robust predictive model than independent flight dates,
effectively summarizing ( R 2 > 0.98)
the UAS flight dates. The logistic model overcame the need to use specific
flight dates when comparing different environments. The UAS height
estimates (r = 0.36–0.48) doubled the correlations to grain yield
in this G2F experiment compared with
PHT TRML (r = 0.23–0.28). Parameters of the
logistical function achieved equivalent correlations (r =
0.30–0.46) to individual flight dates (r = 0.36–0.48), improving
grain yield prediction by ∼400% ( R 2 =
0.25–0.34) over PHTTRML ( R 2 =
0.06–0.08). Incorporating other UAS-derived parameters beyond plant height
may allow yield to be accurately predicted before maturity, speeding
breeding programs. A new public R function to generate ESRI shapefiles for
plot research is also described.