Genetic gain for important agronomic traits can be accelerated in plant breeding by better understanding the environmental effects on field experimental plots over the growing season. Today, aerial image remote sensing via unoccupied aerial vehicles (UAVs) offers cost-effective collection of high throughput phenotypes (HTPs) with high temporal and spatial resolution. However, utilizing HTP to evaluate the genetic merit of tested accessions in a modern breeding program requires effective integration of the captured imagery with genome-wide marker data, experimental design and geographic information, agronomic phenotypic data, and soil and weather data. Furthermore, plant breeders require timely predictions of genetic effects for selecting accessions to advance; therefore, the data should be readily integrated into a quantitative genetics statistical framework. Hence, this dissertation presents four chapters: (1) a database schema for storing genome-wide marker data, (2) a web-database platform for managing plant breeding programs and their experiments, (3) a web-database tool for reliably processing aerial imagery into HTP, and (4) a statistical approach integrating HTP with genomic data to better resolve genetic effects over spatio-temporal environmental effects. In (1), a NoSQL data model is presented within the Chado database schema, utilizing the NoSQL and relational capabilities of PostgreSQL to link the genome-wide marker data and the plant breeding experimental data, respectively. Benchmarking demonstrates computation of a genomic relationship matrix (GRM) and a genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays L. (maize), 314 triploid Musa acuminata (banana), and 924 diploid Manihot esculenta (cassava) samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively. In (2), Breedbase illustrates a web-database platform enabling plant breeders around the world to manage their breeding program data in a standardized process. Importantly, the Breeding API (BrAPI) allows open access and interoperability to the data. Then (3) focuses on ImageBreed as a web-database tool for processing aerial image phenotypes into HTP. Multi-spectral or color imagery from UAVs or from fixed camera systems can be uploaded, processed into orthophotomosaics if required, designated into geospatially referenced plot-polygons, and then summarized into vegetation indices (VIs) or convolutional neural network (CNN) HTP. In (4), the normalized difference vegetation index (NDVI) collected on several years of Genomes-to-Fields (G2F) hybrid maize (Zea mays L.) field experiments is used to improve genomic prediction for grain yield, grain moisture, and ear height. The proposed approach enables greater understanding of spatial heterogeneity in the field and improves the estimation of genetic effects. To conclude, continued aggregation of genomic and image data, coupled with statistical approaches, will enable plant breeders to better understand the stability of genetic effects across space and time. Future research into latent genetic spaces embedded in ground rover lidar point clouds and aerial imagery is an exciting avenue to understanding the permanent environment and genetic stability of accessions.