ASABE/ISO Standard 12188-2 provides test procedures for positioning and guidance systems in agricultural vehicles during straight and level travel. The standard provides excellent descriptions of test procedures, however it does not provide detail on methods to carry out the calculations necessary to calculate relative cross-track error (XTE), which is the primary measurement used to judge accuracy of the system. The standard was used to estimate the guidance accuracy of a relatively low-accuracy vehicle at 1.25 and 0.5 m s-1. At 1.25 m s-1, a nearest point calculation overestimated mean XTE by 0.8 cm, or 8.2%. The location sampling density was much higher with a 0.5 m s-1 travel speed, and mean XTE was only overestimated by 0.1 cm with the nearest point method. Power and energy data were recorded using a sled with a known weight to vary the drawbar force on asphalt. This will allow a comparison between the electric and conventional tractor over a range of forces applicable to a 20 HP tractor. The electric tractor was found to consume less than half the energy compared to a Kubota L5030 in a common configuration and a custom configuration to match the weight distribution of the electric tractor. Finger weeding tasks were recorded throughout the year capturing the duration and frequency of these tasks at the University of Kentucky (UK) consumer supported agriculture (CSA) farm. Power and energy data were recorded from the electric tractor while finger weeding. Diesel consumption was also recorded from a conventional tractor while finger weeding. Field data shows that the electric tractor needs approximately 0.532 kWh of energy while a conventional tractor requires approximately 1.258 kWh or energy to finger weed each row of vegetables. Conventional electric bills were compiled for the University of Kentucky CSA establishing an average monthly electric need. Historic NREL data was compiled establishing an average potential solar resource for central Kentucky. It was determined that a 15 kW photovoltaic array could meet the conventional electric needs of the UK CSA and supply the net energy allowing the electric tractor to meet the finger weeding need.

Real-time water quality monitoring is crucial due to land utilization increases which can negatively impact aquatic ecosystems from surface water runoff. Conventional monitoring methodologies are laborious, expensive, and spatio-temporally limited. Autonomous surface vehicles (ASVs), equipped with sensors/instrumentation, serve as mobile sampling stations that reduce labor and enhance data resolution. However, ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated a commercially available solar powered ASV equipped with a multi-sensor payload ability to operate autonomously to accurately and repeatedly maintain established A-B line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). This work provides a framework for development of spatial/temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of in-situ sampling technologies.