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Satellite Yield Forecast&emdash;Earlier Insight into Corn & Soybean Yield Potential

AGMRI’s Satellite Yield Forecast model gives you a reliable, in-season view of how your crop is performing —well before the combine enters the field. By combining satellite imagery with agronomic intelligence, this offer helps you set expectations, manage risk, and make data-driven decisions during the critical late-season window.

How it Works

Multi-Source Data, One Clear Forecast

AGMRI’s model integrates satellite imagery with weather, soil, topography, and other agronomic data to deliver field-level yield forecasts for corn and soybeans.

  • Season-Long Intelligence. Rather than relying on a single image or point in time, yield forecasts are built using cumulative season data—capturing crop development from emergence through the forecast date for a more complete picture of yield potential.
  • Weekly Updates When It Matters Most. Receive weekly yield forecasts from mid-July through midSeptember, with an expected 4–6 updates per field during grain fill and yield determination.
  • At the end of the season, the model achieved an average accuracy of ~93% at the field level.

What This Means for Your Operation

Field-Level Precision 

Forecasts are generated at the field level, reflecting actual variability across acres—not broad regional averages. 

Actionable, In-Season Insight

Satellite Yield Forecasts help growers: 

  • Set realistic yield expectations earlier
  • Support storage decisions
  • Identify fields that may require closer attention
  • Reduce uncertainty heading into harvest

Start Seeing Value This Season

  1. Sign up for Satellite Yield Forecast through your territory manager.
  2. Add crop type to enrolled fields.
  3. Receive yield forecasts.

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Yield Forecast

Understand where your corn yield is based on the current state of the crop. As the season unfolds, see how it is having an impact on your final yield.

Yield Loss

Powered by years of Nitrogen research at the University of Missouri, our corn Yield Loss analytic, powered by NVision Ag, gives insight into potential yield loss due to Nitrogen deficiency. Optional analytic for nitrogen management.

Variable Dry Down

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Understand which fields and which areas of the field are drying down to help plan your harvest logistics.

Underperforming Area

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Not all areas of your fields perform the same and low NDVI doesn’t necessarily mean there is anything you can do to fix it this year. Underperforming Area alerts you to the fields and areas of the fields that are performing below their historical potential. This will allow you to quickly find those fields and areas and make adjustments to get them back on target and protect yield.

Nutrient Deficiency

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As the crop grows, it can tell us more of what is wrong with it. This analytic finds the fields and areas of the fields where there is a nutrient deficiency so that issues can be addressed before grain fill.

Disease Stress

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In conjunction with the Thermal Stress, Disease Stress alert takes into account weather information to more precisely indicate the type of stress impacting the crop.

Thermal Stress

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Using our thermal imagery, AGMRI can detect elevated heat patterns of the crop that indicate crop stress.

Crop Health

Crop Health

Get a complete view of your farms and fields and identify where yield potential is ranked highest to the lowest.

Weed Map & Weed Escape

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Know what fields and areas of the fields have weeds. With machine integration or based on planting date, be alerted to what fields have weeds that may be impacting yield.

Historical Field Performance

AGMRI creates 5 performance zones in each field based on the historical average of those zones. This data is used to compare the current season to help understand where you are underperforming from the zone potential.

Low Emergence

low emergence

Notification of what fields and areas of the field have poor emergence.

Stand Assessment

strand assessment

AGMRI detects the established rows and uses computer vision and machine learning to determine the best segment of row and compares the rest of the field to that segment to give you a relative map. If machine data is integrated, a stand population map is returned.

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