SalinitySense

Front landing page
Dashboard center

Soil salinity is a "silent killer" for global agriculture. When salt levels rise, land becomes infertile and crops starve for water, costing farmers billions in lost yields. We were inspired to build Salinity Sense because while you can’t see salt with the naked eye, it leaves a distinct spectral fingerprint. We wanted to give farmers a way to visualize their soil health from above before the damage becomes irreversible.

Traditional soil testing is slow, expensive, and requires manual labor in the field. Our dashboard is the ideal solution because it leverages remote sensing. By analyzing how soil reflects infrared light—which differs significantly between healthy and salt-affected ground—we provide real-time spatial intelligence. This allows for massive-scale monitoring that is both cost-effective and immediate. Our development process was anchored in VS Code and managed via GitHub, allowing for a seamless collaborative workflow. To create the user interface, we leveraged AI to blend rapid automated prototyping with significant manual refinement to ensure a professional and intuitive user experience. On the backend, we engineered a Python simulation engine designed to model the specific spectral signatures of soil salinity. This engine accurately mimics how infrared light reflects off salt-affected earth, generating high-fidelity data that is then projected onto interactive geospatial heatmaps. By integrating these technologies, we successfully bridged the gap between raw modeling and actionable visualization.

One of our main hurdles was keeping our development environments in sync. Moving between machines and pulling from GitHub often led to broken dependencies that required constant debugging before we could actually code. We also had to solve problems with data translation, writing custom logic to ensure our backend Python values were correctly formatted for our JavaScript mapping library to render without errors.

In summary, we learned how to bridge the gap between orbital technology and local farming. This project taught us that the most powerful tool for a farmer isn’t just lists of endless data, but the ability to see the invisible through infrared technology, turning high-level satellite science into a simple, actionable visual dashboard.