AI Tools in Natural Resources and Environmental Systems for the Classroom, Research, Extension and Industry

Authors

Derek M. Heeren, University of Nebraska-LincolnFollow
C. T. Agouridis, University of Kentucky
Debabrata Sahoo, Clemson UniversityFollow
Isaya Kisekka, University of California, Davis
Trisha L. Moore, Kansas State UniversityFollow

Date of this Version

7-2025

Document Type

White Paper

Citation

Recommended citation: Heeren, D. M., Agouridis, C. T., Sahoo, D., Kisekka, I., & Moore, T. L. (2025). AI tools in natural resources and environmental systems for the classroom, Extension, and industry. White paper.

Available at: https://digitalcommons.unl.edu/

Comments

Copyright 2025, the authors. Open access

License: CC BY-NC 4.0

Abstract

This white paper synthesizes a series of panel insights on AI adoption across education, Extension, research and industry contexts within natural resources and environmental systems. It emphasizes that while advanced machine learning and generative AI introduce structural shifts in autonomy, scale, and cognitive augmentation, their value lies in augmenting, not replacing, human expertise. Applied tool examples include decision‑support systems for rural water operators, HAB‑diagnosis apps integrating image classification and natural‑language guidance, an irrigation educational chatbot tailored to user background, and sensor‑driven orchard AI for light interception estimation and data gap filling. A recurring design imperative involves co‑development with end users, validation in operational environments, and transparent interpretability to build trust. Reflective discussion addresses governance, data privacy, participatory design, and AI’s potential to enhance accessibility across language, disability, and geography.