Using Uav-Based Crop Reflectance Data to Characterize and Quantify Phenotypic Responses of Maize to Experimental Treatments in Field-scale Research
Abstract
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.
Degree
M.Sc.
Advisors
Camberato, Purdue University.
Subject Area
Agronomy|Aerospace engineering|Robotics|Soil sciences|Transportation
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.