Date of Graduation

5-2026

Document Type

Thesis

Degree Name

Bachelor of Science in Biological Engineering

Degree Level

Undergraduate

Department

Biological and Agricultural Engineering

Advisor/Mentor

Cengiz Koparan

Committee Member

Brian Haggard

Second Committee Member

Terry Howell

Abstract

Surface water monitoring is often constrained by limited spatial and temporal coverage due to the labor-intensive nature of traditional sampling methods, particularly in environments that are difficult to access or pose safety risks. Unmanned aerial vehicles (UAVs) offer a promising solution by enabling more frequent, spatially distributed, and cost-effective data collection. This study presented the design, development, and field evaluation of a UAV-based system for real-time, in-situ water quality monitoring. The system integrated multiple sensors, including oxidation-reduction potential (ORP), RGB spectrometry, pH, electrical conductivity (EC), dissolved oxygen (DO), and a multispectral spectrometer within a UAV platform.

Field testing was conducted at 15 sites at Lake Sequoyah, where spectral measurements were collected alongside laboratory analyses of nitrate and turbidity. Initial spectral-only analysis showed weak correlations, suggesting limitations in the experimental setup, particularly related to controlled lighting conditions. However, results from the fully integrated system suggested moderate correlations between near-infrared spectral bands (760, 810, and 860 nm) with both nitrate concentration and turbidity. These findings suggest that specific spectral ranges may have predictive potential for water quality parameters, and that more advanced statistical methods may reveal relationships not captured through simple correlation.

This work showed the feasibility of integrating multi-parameter sensing systems with UAV platforms and highlights key areas for improvement in sensor design, data processing, and system stability. Future work should focus on refining spectral calibration methods, improving hardware reliability, and expanding dataset size to enhance predictive capabilities.

Keywords

UAV; unmanned aerial vehicle; spectrometer; drone; water quality; sensor integration

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