Theses and Dissertations

Issuing Body

Mississippi State University


Chesser, Gary D.

Committee Member

Lowe, John W.

Committee Member

Padmanava, Dash

Committee Member

Turnage, Lee G.

Date of Degree


Document Type

Graduate Thesis - Open Access



Degree Name

Master of Science (M.S.)


College of Agriculture and Life Sciences


Department of Agricultural and Biological Engineering


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.