Hello, my name is Larry Purcell.
I'm an agricultural scientist with the
University of Arkansas System Division of Agriculture.
And today we're at the Milo J. Shult Agricultural
Research and Extension Center in Fayetteville.
I'll be talking today about research that Trent Roberts
and I have been doing to look to see if corn in early
vegetative stages needs additional nitrogen.
We'll do this with two different methods.
We'll look at the leaf nitrogen concentration
and we'll also look at aerial images taken from a drone
to measure the intensity of greenness.
The drone that we're using is a DJI Phantom 4 Pro.
This drone is directly off the shelf without
any modifications,
it's been a very stable platform for us.
It has a very nice camera that takes
excellent quality images.
It costs roughly around fifteen hundred dollars with the
case and with extra batteries and everything
that you need to fly.
The only thing that you do need is an iPad mini
that goes along with the transmitter.
We and as part of the Division of Agriculture,
we have to have an FAA license in order to fly.
And as a commercial operator on a farm,
you would also need to have an FAA license to fly.
The main things you need to think about,
are you want to make sure you're in an airspace
that's appropriate to fly, that you're below 400 feet,
and that you can always see the drone.
There are several other things that you would
need to know, but those are the primary things.
The software that we use is called Ground Station Pro.
It's also a product of DJI and is freely available.
And, it basically looks a lot like something
that you would see from Google Maps.
You pull a map up on the screen,
you're able to zoom into your farm.
And on the image that I'm showing you now,
it will show the exact spot on the research farm here
in Fayetteville where we'll be flying our field.
You can move the corners of this field to
exactly where you want it,
and then you can plug in, using the software,
your altitude, the amount of overlap you want to have
between images,
and then everything else is ready to go.
All you have to do at that point is just press the fly
button and it's ready to take off,
and that's where we are right now.
So, we're transmitting the data to the drone at this point.
We're ready to fly, and I just have to press the button.
So the drone will be flying at 100 feet,
it will make three passes across the field.
It will take roughly three minutes to do this.
And, during that time period,
it will be taking an image every two seconds.
And so, as it's flying now,
if we look down from the drone at this point,
we can see the bird's eye view of what one pass looks
like as it goes across the field.
The images that we take here today are obviously in a
cornfield that is well past early vegetative stages.
But, the images I'm showing you are from images taken
earlier in the year when the corn was
between V6 and V12.
So it's completed one pass now,
it's about to come back on a second pass.
The drone uses batteries as its energy source,
on one full battery charge,
it can fly around 25 to 26 minutes.
And with this drone flying at 400 feet,
the maximum altitude that you're allowed to fly and with
about a 60% overlap, a farmer or a consultant could
roughly cover somewhere between 80 and 100 acres,
depending upon field dimensions and layout.
So, even though we have a relatively inexpensive drone
running on a battery,
it's still able to cover quite a bit of area
in its flight pattern.
Looking at the drone now,
it is beginning its final pass in the field.
Everything up until this point has
been completely autonomous.
I haven't done a thing other than program it initially
in the laboratory, in my office.
And when it finishes its mission,
it will hover in place and I'll land it manually.
We've collected our data and we're ready to go back to
the laboratory now to pull the images off of the
drone and begin to analyze those images on the
computer and see what these results really mean.
We have taken our drone from the field back to the
laboratory and taken the SD card out of the drone and
have uploaded those files to a computer.
And we are now stitching those images together and
using the software that I've got illustrated
on the screen now.
And what you see are the individual pictures that were
taken in the field and they are placed in their proper
position on the field and over the field based upon their
GPS coordinates that are embedded in each image.
The software that we're using is Metashape
made by Agisoft.
It does a really nice job, there's lots of other programs
that can be used either on your computer
or in the cloud.
I know DroneDeploy has a free service that allows you
to stitch images together.
So many different images can be stitched
together per month.
And then there's a pay service if you go above that
number, but it does a really nice job.
And this picture here,
this screen capture shows a little bit better
where the images are located shows an elevation.
These were all taken at 100 feet above ground level,
shows their position in the field.
And the stitched image looks like this.
And you can see the dark green patches in the field
representing the plots where nitrogen has been applied.
And the yellow portion of the field where insufficient
nitrogen has been applied.
So once we have our stitched image,
we now straighten that image and using software.
And then we import it into another software program
called Field Analyzer.
And this is Field Analyzer, a screenshot from it.
And Field Analyzer allows you to select the number of
rows that we have.
In this case, we have six rows in this portion of the field.
And the number of columns, one, two, three, four.
And this portion of the field that we want to analyze.
And then by double-clicking on one corner of the field
and dragging it over to the other portion of the field,
it selects the plot areas.
And from that,
it extracts the data that we'll use for the DGCI values.
So it's as simple as that.
The DGCI values are then stored in a Excel type sheet,
a spreadsheet that we can analyze just like we
would any other values.
This is another field from Pinetree Research Station
that we evaluated in 2019.
We have lots and lots of different nitrogen treatments
out here and different nitrogen timings on this field.
And you can see the large differences and the intensity
of greenness among these different plots.
I want to zoom in on this portion of the field and
take a little bit closer look at it.
And in this portion of the field,
we have six different nitrogen application rates ranging
from zero all the way up to 270
pounds of nitrogen applied per acre.
And for each of these small rectangles here,
we determined the DGCI value,
the Dark Green Color Index value,
and it ranges from .36 for the zero all the way up to .66
for our highest nitrogen application rate.
One thing that's really interesting is visually,
I cannot tell any difference between the 90
all the way up to the 270.
But, if we look at the DGCI value,
we see that those steadily increase,
including up from 0.57 for the 90 up to 0.66.
So, it gives us something that's much more sensitive
than our eye and determining that the nitrogen
that it has received.
You'll also notice in the upper left-hand corner,
this yellow and these yellow and green boards.
We in the past have used those as kind of internal
standards so that we can correct for differences in
lighting conditions that may affect the DGCI
value in the field.
We have since determined that we can use
the Dark Green Color Index value for our highest
nitrogen treatment and we can express all
the other DGCI values.
Using this as kind of an internal control makes it much
less difficult, especially for in the future,
a producer who may just need to have a high
nitrogen portion of his field, a high nitrogen strip
that he can relate to in the field.
I mentioned earlier in the presentation that in addition
to looking at the DGCI value for determining if corn
needed additional nitrogen.
We were also looking at leaf nitrogen concentration that
was taken from vegetative corn between V8 and V12.
And this is what we found with that analysis.
We're looking at the relative grain yield here versus
the leaf nitrogen concentration.
And what we find is that as the leaf nitrogen
concentration increases from around 1%,
up to 3% we see a steady increase in relative grain yield.
And once we are above around 3%,
we see no further increase in relative grain yield.
So this has been a big step forward in helping us
understand the response of relative grain yield to
leaf nitrogen concentration.
We've done a similar kind of analysis with
the Dark Green Color Index.
It's a little bit more complicated.
And in this figure, we're looking at the relative grain yield
again, and we see the Dark Green Color Index here.
But we also have this other third dimension,
the reference start green color index,
which corresponds to the high nitrogen strip in the field.
But what we see is when the Dark Green Color Index
is high, relative grain yield is high,
and that decreases as the
Dark Green Color Index decreases.
This is also modulated somewhat by the reference
Dark Green Color Index from the high nitrogen strip.
Our next step in this research is to calibrate how much
nitrogen needs to be applied to corn
between V8 and V12 based upon this Dark Green
Color Index and that is research that is ongoing and that
we're working on now.
I want to thank the Arkansas Corn and Grain Sorghum
Board for their support.
Dr. Roberts and I certainly appreciate it.
And we'll be glad to answer any questions.
Thank you very much.
