Grower Decides to Spray Fungicide to Protect Yield Potential


The grower in this case is a seasoned farmer with a history of cultivating this particular field. It was early in the season, and the field faced an emergence issue that initially prompted consideration of replanting. However, based on the emergence map analysis, it was decided to keep the existing crop. Nevertheless, the grower remained concerned about the field’s yield potential, especially as June brought minimal rainfall.


The grower faced a critical decision during the fungicide application stage. Despite receiving an AGMRI disease alert for the field, it was initially excluded from the list of fields to receive fungicide treatment. The decision stemmed from the uneven and slow emergence observed earlier in the season, coupled with the lack of June rainfall. The grower was “on the fence” about whether to spray a fungicide or not, considering the potential yield loss if a disease outbreak occurred.


To address the challenge, the grower collaborated with a retail salesperson who utilized AGMRI’s data. They examined the historical and current field health data, making a detailed comparison. Despite the early-season setbacks, they noticed a significant portion of the field exhibited above-average performance on July 9 compared to historical data. Based on this analysis, the grower made the informed decision to apply fungicide to the field to protect the potential for higher yields.


As a result of this decision, the field is on track to yield 215 bushels per acre, surpassing the historical average for this specific field. The grower expressed appreciation for AGMRI’s role in enabling an objective assessment of the situation and potential course of action. Moving forward, the grower plans to continue using AGMRI’s insights to make data-driven decisions that maximize productivity and efficiency on his farm.


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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

variable dry down

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

Underperforming Area

low crop health

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

nutrient deficiency

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

disease risk

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

thermal risk

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

weed escape

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.