Current wildfire risk assessment methods use stationary automatic weather stations to collect environmental data for risk determination. These devices operate in locations where foliage cover is not present and are not capable of representing the conditions within forested areas. Modeling is the primary method in use to determine the moisture content of dead fuels, yet uses inputs gathered by non-representative automatic weather stations. Accurate fuel moisture determination requires laborious manual sample collection. This research addresses current limitations by developing a suite of devices for use with autonomous all-terrain electric vehicles, capable of collecting input data for fire risk assessment in specific forested locations. The prototype created is capable of autonomous measurement of temperature and relative humidity, wind speed, ten-hour time-lag dead fuel moisture, and soil moisture. The system includes a motorized weather mast for gathering data at a height of six feet, a Cartesian robot for precise positioning of a multifaceted end effector, a fuel sample holding station, and a color based real-time vision system for sample station detection and positioning of the vehicle and Cartesian robot. A theoretical operation scheme is provided for real-world implementation of the system. Testing was performed to determine the feasibility of each subsystem individually and combined in the context of practical application. The study resulted in an overall promising proof of concept, capable of meeting the objective of this research. Limitations presented through testing were found to be resolvable, and recommendations for improvement and future additions to the system are provided.