Director of Business Development Kevin Krieg spoke with the Global Ag Tech Initiative to offer a closer look at how Intelinair’s AGMRI app brings together data analysis, machine learning, and computer vision to deliver valuable insights to manage your fields all season long.
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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.
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.
Understand which fields and which areas of the field are drying down to help plan your harvest logistics.
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.
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.
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.
Using our thermal imagery, AGMRI can detect elevated heat patterns of the crop that indicate crop stress.
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.
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.
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.
Notification of what fields and areas of the field have poor emergence.
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.
