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.
Recommended Citation
Ghumman, Simar, "Wall-E: An Autonomous AI Rover for Precision Agriculture" (2024). Master's Projects. 1459.
https://scholarworks.sjsu.edu/etd_projects/1459