High throughput field phenotyping is a relatively new but rapidly growing research area, and it will remain a top agricultural research priority in the next decade. Remote sensing technologies, proximal sensors, platforms such as unmanned aerial vehicles (UAVs) and ground vehicles, and statistical data-driven analytics are being rapidly customized and deployed for high throughput phenotyping and used as plant performance measurement tools for crop improvement/breeding and precision agriculture systems for agronomy, soil science, and farm management. However, high costs, weather-dependent data collection (e.g., human-operated UAV's), data processing lag from complicated and/or inefficient analysis procedures, and a lack of standardization in sensor-based technologies are just a few of the recurring issues preventing these technologies from being more accessible. Additionally, each newly developed phenotyping technology or tool can measure only one or a few facets of highly quantitative and multi-variable traits in agriculture, such as yield, environmental stressors, or drought resistance.Therefore, the loop needed to make concrete advances in improving our food, fuel, and feed crops remains open with the current agricultural technology platforms. Here, we aim to close the loop by developing and deploying an integrated cyber-physical system for connecting plant phenotypes to genotypes with real-time crop management. With a robust wireless environmental sensor network, this integrated cyber-physical system, or "FieldDock", will deploy and manage daily UAV flights over target fields to automate crop modeling and genetic mapping to accelerate breeding efforts for energy efficient, nutritious, and high-yielding crops while tracking farm inputs to potentially guide crop management.Integrated cyber-physical systems like the proposed FieldDock are vital so that high throughput phenotyping tools are streamlined to be accessible for broad and applied agricultural use. With onboard GWAS and crop model processing, researchers will receive a constant stream of remote data that will allow them to focus on analysis and breeding strategies, rather than manually collecting data throughout the growing season. Breeding efforts across the country, both private and academic, employing the minds of many talented researchers and computer engineers could further fine-tune such a device for many different environments within an ever-changing climate. A standardized all-in-one platform like FieldDock could potentially unify global efforts to accelerate some of the most critical breeding goals of our time by making it affordable and lowering the barrier to entry for such a high end, advanced cyber-physical technology.For farmers, the FieldDock platform aims to connect spatial, temporal and multi-layered environmental data in real time while generating powerful predictive analytics and machine learning models that will drive reliable commands to automate field equipment throughout the growing season. A cyber-physical farm will self-learn with such a system in place and adapt to keep pace with the rapidly changing climate and the unpredictable challenges it will bring. FieldDock will act as an all-encompassing platform to gather all crucial field data needed to offer decision support for farmers in the short term while developing machine learning models from detailed datasets for the autonomous farm of the future.Ultimately, the proposed project will collect plot level data at a spatial and temporal resolution necessary for researchers and growers to develop and improve high-yielding, energy efficient crops that are resilient to variable climates, and also benchmark an integrated closed-loop smart farm system that can help agricultural growers reduce their energy inputs in real time.