CHS Spongebob

New Mexico often faces severe water scarcity, which makes resource preservation important for its agricultural communities. Our project aims to develop an affordable and accessible method for detecting soil moisture across both small-scale and large-scale plots, with the end goal of providing access to vital agricultural systems to low-income farming regions.

To begin, we will first design and test a Ground-Penetrating Radar (GPR) system – which will be mounted on an unmanned vehicle – that operates within the 845-885 MHz frequency band to analyze soil moisture and composition. This system will be built around our 2025-26 Supercomputing Challenge entry: “CHS Avalanchers”, repurposing its existing antenna system and adding mounting supports.

During our initial testing, we will gather radar samples under controlled moisture conditions to train a machine-learning classification algorithm. Once our prototype is validated to be functional, we will miniaturize the system for better mobility, refine our data analysis tools, and expand the machine learning model to provide further mapping analysis tooling. The ultimate goal is to develop a scalable and low-cost agricultural tool that will help farmers optimize their water usage.