Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Autonomous in-flight aerial refueling is an important capability for the future deployment of unmanned aerial vehicles, because they will likely be ferried in flight to overseas theaters of operation instead of being shipped unassembled in containers. A reliable sensor, capable of providing accurate relative position measurements of sufficient bandwidth, is key to such a capability. A vision-based sensor and navigation system is introduced that enables precise and reliable probe-and-drogue autonomous aerial refueling for non-micro-sized unmanned aerial vehicles. A performance robust controller is developed and integrated with the sensor system, and feasibility of the total system is demonstrated by simulated docking maneuvers with both a stationary drogue and a drogue subjected to light turbulence. An unmanned air vehicle model is used for controller design and simulation. Results indicate that the integrated sensor and controller enables precise aerial refueling, including consideration of realistic measurement errors and disturbances.

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April–October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for within-season data collection of agricultural crops such as sorghum.

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Dynamic inversion is a control synthesis technique in which the inherent dynamics of a dynamical system are canceled out and replaced by desired dynamics, selected by the designer. The output of such an inner-loop controller is the control input, which produces the desired closed-loop response. The desired dynamics essentially form a loopshaping compensator that affects the closed-loop response of the entire system. This paper attempts to quantify the effect of different forms of desired dynamics on the closed-loop performance and robustness of a dynamicinversion ight controller for reentry vehicles. Proportional, proportional-integral, ying-quality,and ride-quality forms of desired dynamics are evaluated using time-domain speci cations, robustness requirements on singular values, quadratic cost, and a passenger ride comfort index. Longitudinal controllers are synthesized for a generic X-38 type crew return vehicle, using a set of linear models at subsonic, transonic, and hypersonic ight conditions. For the candidate forms of desired dynamics and inversion controller structures evaluated here, results indicate that the form used impacts closed-loop performance and robustness and more so for some inversion controller structures more than others. The ride-quality dynamics used with a two-loop angle-of-attack inversion controller provide the best overall system performance, in terms of both time-domain and frequency-domain speci cations, and the evaluation criteria. Jennifer Georgie of Victoria, Texas, earned the B.S. and M.S. degrees in aerospace engineering from Texas A&M University in 1999 and 2001, respectively. She is a recipient of the Distinguished Student Award for Outstanding Academic Achievement from the College of Engineering. From 1996 to 1998 she served three coop tours at the NASA Dryden Flight Research Center, working in the X-31 Flight Controls Group and on the Russian High Speed Civil Transport program in the Aerodynamics Group. For her efforts she was awarded the NASA Dryden Stephen B. Davis Outstanding CoOp Award and the NASA Dryden Spotlight Award twice. From 1999 to 2001 she was a graduate research assistant at Texas A&M, where she worked on ight testing a vision-based automatic landing system and on dynamic inversion controllers for reentry vehicles as a graduate summer intern with the NASA Johnson Space Center. Since June 2001, she has been with Lockheed Martin Aeronautics Company, Fort Worth, Texas, where she is a ight control engineer in the F-16 Block 60 Control Law Design and Analysis group. She is a Member of AIAA.