Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
Cite this dataset
Anderson, Steven et al. (2019). Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems [Dataset]. Dryad. https://doi.org/10.5061/dryad.3295k54
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.