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Crop Yield Prediction

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.

satellite collecting yield data

Crop Yield Prediction in Numbers

up to 95%

Accuracy of yield estimated depends on the quality of statistical data and can vary from 85% to 95%.

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.


Our Approach

For maximum efficiency and accuracy of crop yield forecasting, we fuse two different types of yield prediction models - biophysical and statistical. This "hybrid" approach allows us to take on more complex projects.

Biophysical yield prediction model

  • Collect data (weather parameters, soil analysis, crop state, phenological data, etc.).
  • Calibrate the model and carry out the LAI assimilation to ensure accuracy of a crop yield forecast in the absence of statistical data and to increase the variability of values.
  • Simulate the biological productivity parameters (TAGP, WSO, relative soil moisture, total water consumption, and others) to estimate yield.
  • Update the data once every 14 days to increase the accuracy. This has to do with weather updates.

Statistical yield prediction model

  • Collecting data to create a crop yield prediction dataset and combining it with possible predictors (rainfall, temperature, humidity, soil type, and others).
  • Picking the right ML model for the project - e.g. Linear regression, Random Forest, LightGBM, XGBoost, CatBoost, to name a few.
  • Adjusting the model to answer the specific needs of the project in question for best results.

Model fusion stage

The fusion stage is necessary if we want to achieve the highest possible accuracy of 95%. We fuse the biophysical yield prediction model with the statistical model described above.

EOS Crop Modeling + LAI Assimilation

  • Ensemble of model scenarios

  • Asquisition 1

  • Asquisition 2

  • Asquisition 3Selection of most likely scenario and re-initalization of the modelled ststem state with the scenario

  • Asquisition 4Observations of LAI

  • Asquisition 5

  • Harvest

Applying LAI assimilation allowed us to achieve the 95% accuracy in 30% of the fields. For the fields marked in red, the accuracy of less than 80% was achieved, while the crop yield forecast accuracy for the green-marked fields exceeded the 80% mark.

EOSDA Crop Modeling accuracy
EOSDA Crop Modeling
EOSDA Crop Modeling + LAI assimilation accuracy
EOSDA Crop Modeling + LAI Assimilation
Use Cases

Our Success Stories

Yield forecast for a large agroholding in Ukraine

In 2020, we implemented a yield prediction project for 6 major crops: Winter Barley, Winter Rapeseed, Winter/Spring Wheat, Sunflower, Soy, and Maize.

Two different reports were generated:

  • 45 days prior to the harvest
  • 2 weeks prior to the harvest.
  • less than 80% accuracy
  • more than 80% accuracy
WOFOST Yield prediction
WOFOST Yield prediction
EOSDA Yield prediction Machine Learning Model WOFOST (inputs/outputs) + LAI (Sentinel-2)
EOSDA Yield prediction Machine Learning Model WOFOST (inputs/outputs) + LAI (Sentinel-2)
Accuracy (Wofost) Accuracy (Wofost + Lai)
Maize 0.75 0.91
Soy 0.78 0.86
Sunflower 0.71 0.88
Winter barley
Winter barley 0.53 0.82
Winter wheat
Winter wheat 0.75 0.92
By improving the model with LAI assimilation, which was developed by the team, we managed to increase the accuracy of yield estimation in 30% of the fields compared to the traditional WOFOST approach.

The table below shows the correlation between the accuracy of yield estimation, the target crop and the number of fields. For example, the predicted yield of winter barley was more than 90% accurate for 52 fields.

Number of Fields
Crop / Accuracy
Crop / Accuracy <70% 70-75% 75-80% 80-85% 85-90% >90%
Winter barley

Number of Fields

Winter barley 27 7 5 22 23 52
Winter wheat

Number of Fields

Winter wheat 33 17 19 21 19 102
Winter rapeseed

Number of Fields

Winter rapeseed 26 6 20 14 27 22

Number of Fields

Sunflower 12 11 12 14 19 22

Number of Fields

Soy 28 22 29 58 37 86

Crop yield forecasting for a Canadian insurance company

Goal: Reliable predicted yield data on every customer to reduce insurance risks.

Input data: Over 100 fields on 20 farms.

Task 1.
Estimating average yield for 6 major crop types growing in every field on all 20 farms and comparing it against the actual yield report.

yield estimation model vs actual yield by crop type given in %

  • Canola > 98,03
  • Corn > 87, 59
  • Peas > 76,25
  • Soybean > 95,94
  • Sunflower > 98,21
  • Wheat > 98,63
Crop Modelled Yield Actual Yield
Canola 41,81 41,00
Corn 123,65 110,00
Peas 30,94 25,00
Soybean 22,89 22,00
Sunflower 1767,73 1800,00
Wheat 53,72 53,00
Grand Total
Grand Total 95,6 94,47
Task 2.
Estimating yield 14 days prior to the 2020 harvest.

yield estimation model vs actual yield by crop type given in %

  • Canola > 96,96
  • Corn > 91,69
  • Oats > 99,98
  • Rye Fall > 85,85
  • Sunflowers Confects > 85,36
  • Sunflower Oils > 98,06
  • Wheat > 94,95
Crop Modelled Yield Actual Yield (Farm 4)
Canola 40,19 39,00
Corn 119,14 110,00
Oats 125,03 125,00
Rye Fall
Rye Fall 64,39 75,00
Sunflowers Confects
Sunflowers Confects 2063,60 1800,00
Sunflower Oils
Sunflower Oils 1834,19 1800,00
Wheat 61,73 65,00
Grand Total
Grand Total 584,34 528,00
Task 3.
Providing the client with the crop yield forecasting data to enable more efficient planning of crop rotation and, as a result, significantly reduce insurance risks.

The graph shows the predicted yield for target crops in 3 selected fields in bushel/ha.

  • SE-2-6-28-W1

    Field name
    Sunflower Oils
  • SW-36-7-28-W1

    Field name
    Sunflower Oils
  • W-34-5-27-W1

    Field name
    Sunflower Oils
Field name Canola Corn Soybean Sunflower Oils Wheat
SE-2-6-28-W1 58,68 194,33 41,45 2208,85 72,49
SW-36-7-28-W1 30,91 169,49 14,42 1146,91 46,24
W-34-5-27-W1 38,77 151,58 24,71 1476,83 59,82
The Jackknife resampling technique was used. Namely, by systematically omitting every observation from a dataset we calculated the estimate and then discovered the average of the calculations. To exclude climatic and technological factors, we only used the data for the past 6 years.

The harvest period for the target crops in Canada usually lasts from August till September. Knowing this, we were able to forecast yield two months before the harvest, achieving an accuracy of over 82%. The accuracy was steadily increasing as the harvest was approaching until it reached 90% just two weeks prior to the harvest, as had been expected.

EOSDA is a leader in the application of earth observation technologies for business and environmental purposes in 22+ industries, making a special emphasis on agriculture and forestry. So far, over 900 000 customers worldwide have benefited from the EOSDA satellite monitoring products. We are about to launch our own satellite constellation that will provide imagery in 13 spectral channels for agronomists all over the globe. You can task us with crop yield estimation for an area as small as a field and as large as a country. Using 10 separate sources of data along with trained neural network models, our team of RnD scientists will help your business thrive sustainably.

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