<p>Unmanned aerial vehicles (UAV)
have revolutionized data collection in large scale agronomic field trials (10+
ha). Vegetative index (VI) maps derived from UAV imagery are a potential tool
to characterize temporal and spatial treatment effects in a more efficient and
non-destructive way compared to traditional data collection methods that
require manual sampling. The overall objective of this study was to
characterize and quantify maize responses to experimental treatments in
field-scale research using UAV imagery. The specific objectives were: 1) to
assess the performance of several VI as predictors of grain yield and to
evaluate their ability to distinguish between fertilizer treatments, and the
effects of removing soil and shadow background, 2) to assess the performance of
VI and canopy cover fraction (CCF) as predictors of maize biomass at vegetative
and reproductive growth stages under field-scale conditions, and 3) to compare
the performance of VI derived from consumer-grade and multispectral sensors for
predicting grain yield and identifying treatment effects. For the first
objective, the results suggest that most VI were good indicators of grain yield at late vegetative and early
reproductive growth stages, and that removing soil background improved
the characterization of maize responses to experimental treatments. For
objective two, overall, CCF was the best to predict biomass at early vegetative
growth stages, while VI at reproductive growth stages. Finally, for objective
three, performance of consumer-grade and multispectral derived VI were similar
for predicting grain yield and identifying treatment effects.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12993872 |
| Date | 16 December 2020 |
| Creators | Ana Gabriela Morales Ona (9410594), James Camberato (9410608), Robert Nielsen (9410614) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Using_UAV-Based_Crop_Reflectance_Data_to_Characterize_and_Quantify_Phenotypic_Responses_of_Maize_to_Experimental_Treatments_in_Field-Scale_Research/12993872 |
Page generated in 0.0015 seconds