• AgMRI
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    or 833-My-AgMRI (833-692-4674).

Anatomy of AgMRI

NDVI

RGB

CIR

Thermal

Anomalies

Scouting and Reporting

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FAQ

 

What crops does AgMRI analyze?
We are currently working with corn and soybean growers, and have also partnered with leading cotton, barley, and coffee growers to validate AgMRI’s use in these crops.
Where is AgMRI available?
AgMRI is available in many states across the Midwest and Great Plains.
How do you collect imagery?
We collect imagery via manned aircraft. While satellite and drone imagery have their places, we have found collection via plane to satisfy both our need for scalable image acquisition as well as ultra high-resolution imagery.
What is the resolution of your imagery?
Our RGB imagery (standard color imagery) is 20 cm/pixel, and our RGN, the imagery on which our analytics are based, is at 10 cm/pixel.
How early and late in the season do you collect imagery?
We believe there is value in collecting imagery throughout the season. Bare soil imagery provides insight into planting conditions, tile lines/drainage, and weed pressure. Collecting imagery often through the early to mid-season helps identify emergence issues/replant opportunities, weed pressure, compaction, nutrient deficiencies and disease/insect pressure. In the late season, we typically capture imagery every 2-3 weeks to identify areas of late-season disease and for harvest timing purposes.
Can AgMRI be used to model yield potential?
Yes! Imagery throughout the season can help indicate/estimate yield:

– Early season, first month post planting → Up to 30% of yield can be determined during this early period. The vegetation row health image provides a comprehensive view of areas with a weak stand where you can expect decreased yield potential.

– Late June – V10/12 → This is a prime time to see the impact of compaction and other issues on yield through the anomaly algorithm which highlights these areas.

– Pre/Post-pollination → Understanding the condition of your corn crop coming into and out of pollination as well as uniformity of pollination is also a great way to understand yield potential.

– Grain fill → Instead of pulling ear samples from random areas of the field you can sample based on anomaly zones and calculate a much more accurate yield estimate for the field.

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