Author

Simar Ghumman

Publication Date

Fall 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Mark Stamp

Third Advisor

William Andreopoulos

Keywords

Unmanned Ground Vehicles (UGVs), machine learning, computer vision

Abstract

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate these time-consuming, labor-intensive tasks so that Wall-E can support farmers in making data-driven decisions. Furthermore, Wall-E provides a foundation for advanced machine learning techniques as it captures the world around it.

Available for download on Wednesday, December 31, 2025

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