Open AccessPrediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems
Author(s) -
Anderson Steven L.,
Murray Seth C.,
Malambo Lonesome,
Ratcliff Colby,
Popescu Sorin,
Cope Dale,
Chang Anjin,
Jung Jinha,
Thomasson J. Alex
Publication year - 2019
Publication title -
the plant phenome journal
Language(s) - English
Resource type - Journals
ISSN - 2578-2703
DOI - 10.2135/tppj2019.02.0004
Subject(s) - altitude (triangle) , fixed wing , yield (engineering) , environmental science , statistics , biology , mathematics , wing , materials science , geometry , engineering , metallurgy , aerospace engineering
Core Ideas UAS captured increased genetic variation compared with manual terminal height. There were small significant differences in ground filtering methods to extract plant structure. Higher resolution did not improve imagery informativeness with regard to plant height. Logistic function provides informative phenotypes for temporal maize growth. Correlation and prediction accuracy of grain yield increased by ∼20% with UAS heights. 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 PHT TRML ( 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.