Predicting Crop Yields with Satellite Imagery and AI
Agricultural production is the quiet engine of national security. Yet the systems we use to forecast crop yields—one of the most foundational metrics in global supply chain stability—are fundamentally reactive. Today, we don’t know how much corn we have until trucks are weighed. That’s too late.
Strike Labs proposes a better way: a scalable AI system that predicts crop yield at high resolution, months in advance of harvest, using nothing more than satellite imagery and weather data.
Why It Matters
Every acre of farmland behaves like a sovereign system. Weather patterns differ county to county. Soil properties vary by meter. Crop rotation schedules shift each year. Equipment constraints force staggered planting. The result is a highly variable landscape where yield is hard to model—even at the local level.
Yet national-level consequences hinge on this variability. Low-yield years disrupt exports, increase food prices, and strain reserves. During the COVID-19 pandemic, weaknesses in agricultural forecasting revealed how brittle the supply chain can be.
The Technology
We developed a deep learning model that ingests remote sensing data and historical weather patterns to forecast yield before a single ear is harvested. No proprietary inputs. No surveys. Just globally accessible inputs, processed through an architecture built to scale.
When tested at the county level across multiple growing seasons, the model demonstrated strong predictive accuracy months ahead of market delivery. With access to enhanced imagery and classified soil/weather overlays available to the Department of Defense and Intelligence Community, performance could improve significantly—down to acre-level resolution.
Strategic Use Cases
This model isn't just a forecasting tool. It’s a planning instrument. Potential applications include:
Setting food reserve levels based on forward-looking data
Identifying at-risk regions for food insecurity or market intervention
Informing crop insurance risk profiles and futures trading strategy
Supporting allied nations with yield prediction for stabilization efforts
Enabling preemptive logistics planning for FEMA, USAID, and state agencies
In a world where global food supply is increasingly tied to geopolitical stability, predictive agriculture analytics is no longer a luxury. It’s an operational requirement.
This initiative was proposed to the Joint Artificial Intelligence Center (JAIC) as part of Strike Labs' commitment to safeguarding America’s food resilience through applied machine learning.
