Characterizing the impact of genetic and environmental variation on maize utilizing phenomic approaches

Abstract

To gain a better understanding of how genotypic elements interact with the environment, we need to develop more efficient ways of monitoring plant phenotypes so that we can gather phenotypic data through time. Remote sensing technologies provide effective means to do this. Sensor, computational, and platform technologies keep evolving and provide avenues to evaluate how plants grow and respond to environmental conditions. The goals of this thesis were to develop methods and analytical procedures to evaluate maize plant phenotypic performance under varied environmental conditions. The first set of experiments were geared towards utilizing unmanned aerial vehicles equipped with RGB cameras to assess how maize plants grow in response to various field management conditions, including various planting dates and densities, and weather events. These tools were used to assess variation for lodging responses and downstream impacts on productivity. More generally, results from this thesis demonstrate that measurements collected early in development can be useful for improving predictions of end-season traits. The second set of experiments provided insights into using hyperspectral technologies for genotypic differentiation and abiotic stress detection. There is a large amount of variation in reflectance throughout maize leaves that can be useful in distinguishing different maize genotypes at the seedling stage and for detecting and quantifying abiotic stress conditions including cold, heat, and salt stress early in development. By documenting phenotypic differences across genotypes and environments through time in a more efficient manner by taking advantage of available remote sensing technologies, we can improve our understanding of how different environmental and genetic elements impact plant productivity and facilitate advancements in crop improvement and production.