Crop Yield Prediction

Crop yield estimation using remote sensing and machine learning models.

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EOSDA team of data scientists and engineers has developed effective techniques for crop yield estimation using remote sensing and machine learning models. We’re relying on earth observation data retrieved from satellites to cover areas ranging from individual farms to regions

Crop Yield Prediction in Numbers

Accuracy up to 95%
Accuracy of yield estimated depends on the quality of statistical data and can vary from 85% to 95%.
Forecasts up to 3 months ahead
Current season yield forecasts up to 3 months in advance.
Crop types 100 +
Yield predicted for over 100 crop types.
Project speed up to 14 days
We’ll produce a 95% accurate yield forecast in two weeks or less, depending on the complexity of the project.
Entries per crop 0 to 100 fields
WOFOST yield estimation model requires no data at all.
Data Sources 10 +
We make sure the forecasts are based on the most comprehensive data analysis.

Yield Estimation Benefits

Increased speed of decision-making related to harvesting, storing, and transporting operations.
Data on crop profitability in your area of interest based on yield estimation.
Opportunity to strengthen global food security by introducing crop yield forecasting to developing countries - helping them to prevent famine, boost local economies, and implement sustainable agricultural practices.
Improved understanding of the agricultural market and better-informed decisions on the management of stocks, imports, and exports, in accordance with CAP and other similar policies.
A much better understanding of cumulative effects of hostile field conditions (pests, diseases, nutrient deficiencies, and others) on crop development.