AgriSure

Inspiration

Every planting season, farmers make one of the most important financial decisions of the year. What crop should they grow?

That decision determines revenue, insurance outcomes, and overall financial stability. Yet many crop choices are still based on tradition, last year’s results, or what neighboring farms are planting.

At the same time, climate volatility is increasing and insurance payouts continue to rise. The data exists. Historical yields, weather patterns, and insurance loss records are all available. However, they are rarely connected in a way that helps farmers make informed, risk aware decisions.

We built AgriSure because we believe farmers are not just choosing crops. They are choosing financial risk profiles. We wanted to create a system that makes that risk visible and understandable.

What it does

The system follows a structured pipeline.

First, we aggregate historical yield data by crop and county to understand long term productivity and variability.

Second, we integrate climate information such as temperature and precipitation patterns to capture environmental volatility.

Third, we analyze insurance loss ratios to understand historical financial exposure.

Finally, we combine these signals into a risk adjusted ROI score. This allows farmers to compare crops based on expected return, stability, and downside exposure.

The question shifts from “Which crop yields the most?” to “Which crop offers the best balance between return and risk?”

How we built it

AgriSure is a crop ROI and risk intelligence engine. It integrates three core datasets:

Historical crop yield data over the past 20 years
Long term climate variability data
Crop insurance premium and indemnity records

Instead of focusing only on yield prediction, AgriSure evaluates both performance and stability. It measures how a crop has performed historically, how volatile it has been, and how often it has triggered insurance losses.

The result is a risk adjusted comparison of crops at the county level. Farmers can see not just which crop produces higher returns, but which one offers stronger financial resilience.

Challenges we ran into

The biggest challenge was aligning datasets that were structured very differently. Yield data, climate records, and insurance information all operate at different spatial and temporal resolutions. Bringing them into a unified framework required careful aggregation and validation.

Another challenge was scope management. With limited time, we chose to focus on a small set of counties to ensure correctness and clarity. This decision allowed us to prioritize robustness over raw scale.

Accomplishments that we're proud of

One of our biggest accomplishments was successfully integrating three very different datasets into a unified decision engine. Yield data, climate data, and insurance records are structured differently and exist at different levels of granularity. Bringing them together in a clean, consistent framework required careful aggregation, normalization, and validation.

Another accomplishment was building a working end to end prototype within a short time frame. The system takes a location input, maps it to county level data, compares crops using historical performance and risk metrics, and produces a clear recommendation. Even with a limited number of counties in the MVP, the architecture is scalable and ready to expand.

Most importantly, we are proud that AgriSure connects technical data analysis with a real world financial decision that affects farmers every season.

What we learned

We learned that data integration is often harder than model building. Real world datasets rarely align neatly.

We also learned that averages alone do not tell the full story. Volatility and downside risk matter just as much as expected returns.

Most importantly, we learned that agricultural decisions are fundamentally financial decisions made under uncertainty. Tools that clarify that uncertainty can significantly improve resilience.

What's next for AgriSure

The next step for AgriSure is expanding geographic coverage. The current MVP focuses on a select set of counties to ensure clean integration, but the system can scale to statewide or national coverage.

We also plan to incorporate forward looking signals such as seasonal climate forecasts and real time commodity pricing. This would allow AgriSure to move from historical risk analysis toward predictive financial planning.

Another major direction is deeper insurance integration. By partnering directly with insurers, AgriSure could help recommend optimal coverage levels and support proactive risk mitigation instead of reactive claim processing.