Date of Graduation
Fall 2025
Degree
Master of Science in Biology
Department
Biology
Committee Chair
Ligon Day
Abstract
Natural aquatic systems are characterized by complex ecological processes and interactions occurring across multiple scales, and quantifying them is essential for proper management. However, managers often face decreasing budgets, increasing responsibilities, and limited staffing, resulting in challenges related to data prioritization, digitization, and management. While modern analytical technologies offer powerful tools, their effectiveness relies heavily on timely and accurate data input from diverse sources, including written, audio, and visual formats. Traditional data management practices are time-consuming, particularly due to the need for manual data verification. To address these challenges, innovative technologies leveraging artificial intelligence (AI)—including deep learning, machine learning, and neural networks— were evaluated for their potential to streamline data management. Three AI-powered prototypes were developed to automate the conversion of written, audio, and visual data to structured spreadsheets. Each prototype was tested using both small and large sets, including older datasets to assess improvements associated with software updates. Results demonstrate the utility of AI technologies to enhance data management efficiency and effectiveness, ultimately benefitting managers who oversee natural resources.
Keywords
artificial intelligence, data management, natural resources, data entry, Amazon AWS
Subject Categories
Aquaculture and Fisheries
Copyright
© Jonathan Fisk
Recommended Citation
Fisk, Jonathan, "Advancing Natural Resource Management Through Artificial Intelligence" (2025). Graduate Theses/Dissertations. 4111.
https://bearworks.missouristate.edu/theses/4111
