AGMRI Weed Alerts Transform Field Management for Central Indiana Grower

On June 15, 2023, a grower in Central Indiana, utilized AGMRI’s weed alerts to optimize their field management strategy. The grower manages multiple fields and was facing the challenge of efficiently prioritizing weed control efforts.

The grower was dealing with weed issues across several fields and needed to determine which areas were most affected and required immediate attention. The grower was also replanting fields, which was his top priority. Because of this, weed pressure increased making the decision of which fields to spray first even more important. Failure to address this problem could potentially result in significant yield loss and decreased productivity.

AGMRI weed alerts provided crucial data, indicating that approximately 40% of the fields were affected by weed pressure. To address this challenge effectively, the grower used the size and severity of the alerts to determine which fields needed to be sprayed first. This approach allowed them to identify and address weed issues efficiently and systematically.

This grower’s experience with AGMRI weed alerts proved to be a game-changer. Communication between retailer and grower regarding AGMRI weed alerts allowed the grower to effectively manage weed pressure by tackling the biggest problems first. This strategic approach not only saved time and resources but also resulted in improved field productivity and crop yield potential. Based on this success, the grower plans to continue using AGMRI and its data-driven solutions to make informed decisions and further enhance their field management practices, ultimately improving efficiency to positively affect their bottom line.


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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.