Cropernicus

💎 Idea

Commodity traders, seed and fertilizer industry forecast the yield/necessities of any crop production based on local state-owned agencies’ monthly reports. 

For example: an industry commodity leader, Archer Daniels Midlands, on their weekly report, needs to digest USDA’s National Agricultural Statistics Service’s monthly crop predictions, based on past rainfall. 

Our solution predicts crop production using satellite data, providing daily updated information about any given area and commodity. 

Scientific literature and our early machine learning model show a good correlation between Sentinel-2 data, water availability and past crop yields. We are training our model to forecast production based on this newly-available technology. 

Our competitive advantage is giving independent real-time future yield predictions to stakeholders in an easy-to-consume way.

🛰️ EU space technologies

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) obtained from multispectral Sentinel-2 products are the core of our product: we are building value based on this technology.

🏦 Space for the Financial World

We address challenge #3: Advancing global finance intelligence by providing real time and applicable insight to commodity traders and other stakeholders.

🤼 Team

Kiko Cáliz: CEO. 

Machine learning engineer. Experience as CEO and CTO of two successful startups. 

Carlos Vivar: CTO.

Earth Observation data scientist. WEkEO’s jupyter notebook competition winner.

Antonio Merino: COO. 

Civil engineer and stock market investor. 

Demo Dashboard and website

https://caviri-cropernicus-dashboard-streamlit-app-zyzisn.streamlit.app/

cropernicus.github.io