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- It's this huge robot
that just goes along rails
and take pictures of plants.
I call it the plant robot, and inevitably,
the next question that
they always ask is why?
Why do you take pictures of plants?
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- [Narrator] Arizona is described by some
as having the climate of tomorrow today.
- If we think about corn,
if you want to see how
it responds to drought,
you need to bring it to an area like this.
You know, they don't build
Volkswagens in Maricopa, Arizona,
but they bring 'em here to
test because it is so extreme.
- [Narrator] The crops
growing here can do so
in upwards of 115-degree heat,
often with limited water.
So as the world's climate changes,
how will farmers grow
the crops of the future?
These scientists want to figure that out.
- Thinking about the future,
we need to start planning for
it now because if we don't,
we're gonna arrive at
a situation in 50 years
where we're not equipped to deal with it.
Basically, it'll change the way we live.
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- Does it got harder
to like kill them off.
I'm assuming when the go
deeper in at that point.
(men chatting)
- [Narrator] According to researchers,
this is the largest
agricultural robot in the world.
It's collecting data from
crops here in Maricopa
as part of a collaborative effort
between research institutions
to better understand
plant genetics and plant
responses to the environment.
A team of scientists and engineers
from the University of Arizona manage
the day-to-day operations
of the field scanalyzer
and help process the data it collects.
The project has been running since 2016
with funding from the Department of Energy
and the Bill and Melinda Gates Foundation.
The project aims to help
produce stress-resilient crops,
ones that could thrive in
warmer and drier climates,
or help produce biofuels to
reduce reliance on fossil fuels.
- Specifically like these ones, you know,
we're getting some
extensive seeding damage.
The climate models predict that, you know,
a hundred years, surface
temperature will be increased
by five degrees Centigrade.
That's a lot different
than what we have now,
and we aren't going to be able
to develop plants fast enough
to meet that change in the environment.
- [Narrator] Scientists are still trying
to figure out how likely that is,
but Pauli and his team are
hoping to produce plants
that can withstand the warmer temperatures
forecast by several climate models.
- The long-term goal is understanding
how we're going to address this problem
of understanding drought resiliency.
You'll notice it has this
large bank of LED lights.
How this camera operates
is it basically sends out
a saturating pulse of light that quenches
all the photosystems within the plants,
and then when we take a series
of about a hundred images
in about two seconds.
- [Narrator] The 30-ton scanner,
referred to by the team as the gantry,
uses sensors and cameras
to capture detailed images
of the width, length, shape,
and angle of individual plant leaves.
A single scan can generate
more than 15,000 individual photos.
- The information we
can gain from the gantry
is we can try to see like
which, which of these
varieties behind me is most
efficient with sunlight?
Which one's best at
taking that natural energy
and converting it into biomass?
That's what we really want.
We wanna identify those
plants that can do that
with the least amount of water possible,
and so those are the types
of transformative insights
that we're really looking
for in terms of addressing
some of these larger problems.
- [Narrator] Paulai hopes
that the data collected
from the gantry will be transferable,
not just to other crops,
but to other localities.
- The technology driving this project
is really not anything special,
but it's more about the integration
of all these various different components.
What I hope it'll enable?
Sustainable farming,
sustainable production
of nutritious calories for people.
I mean, that's the goal
of this whole project
is improving the human condition.
- [Narrator] Another
crucial part of the project
happens two hours away in Tucson.
- This is where.
- This one was at-
- [Narrator] Eric Lyons leads
a team of students who grapple
with the thousands of images
that come off the gantry on a daily basis,
creating up to 10
terabytes of data per day.
- Once we have those coordinates,
we can then go back to our
3-D images and say, okay,
I want a crop of this specific area,
and that's what you're seeing here.
- One of the key challenges,
especially with data coming
off the gantry system,
is we're dealing with
sensor data from about
half a dozen different cameras as well as
a bunch of environmental sensors,
so we take all of these data in,
all coming from different cameras,
all requiring different
kinds of data processing.
- [Narrator] Part of that processing
involves machine learning.
Lyons' team is dealing
with thousands of images
on a daily basis.
Machine learning algorithms help them
stitch these photos
together as well as identify
individual plants and track
their progress over time.
- Ideally, we'd love to
have predictive models.
Often, these are going to be based
on machine learning
methods in order to say
before we plant a plant,
how is this gonna perform,
given some scenario,
over its growing season?
How will it perform in a storm?
How will it perform in a drought?
How will it perform with
excessive heat days?
How will it perform if it's
particularly cold and damp?
- [Narrator] While
algorithms are making the job
of processing this data easier,
the project still requires a human touch.
- That was the case.
- [Narrator] Emmanuel
Gonzalez is a grad student
at the University of Arizona.
He splits his time between
the computational side
of the project in Tucson and working
with the crops in Maricopa.
- It's really important to
have an exposure to both.
There's the whole computational aspect,
but there's also spending
time with the plants.
There's a sense of other
information that you get
just from being here.
The smaller details, such
as how close are the plants,
you know, how do they feel?
My hope is that, as a
breeder in the future,
I can collect data that can inform growers
into growing their crops more sustainably,
and what that would
hopefully translate into
is also making this technology
available widespread,
not just to farmers here
in the United States,
but also casting a wider net,
right, throughout the world.
My family grew up in Mexico, right,
and we have very similar weather.
I don't really like to
focus in on one thing.
I always like to see things
from a global perspective.
- So this is 30 days out from flowering.
So basically right now, our
biggest risk is ensuring
that we get the plants to actually flower,
and so any times this type of damage
just begins to like accumulate-
- [Narrator] It's still early days,
but the team here hope that
this data could help the farmers
of the future grow successful
crops in what could be
increasingly challenging conditions.
- If we do our job well,
the gantry will be obsolete,
and I say that not from anything jaded.
It's just the fact that that
means that we've been able
to take the technology,
the instrumentation,
and we've been able to make it small,
democratization basically.
How do we take some of
the cameras on board,
make them small, and
put them in the pockets
of subsistence farmers
or any farmer really
to basically improve crop production?
- I think in the future,
farmers are going to rely
increasingly more on technology.
I mean, at the moment they already do.
There are sensors that
they mount on tractors,
and they have subscriptions to a variety
of different geospatial
technology out there,
and that's just going to increase.
I think the farmer of the
future is going to not only
be able to know how to manage
a field and work the plants,
but also be able to wrangle
the technology and be able to
make informed decisions from that.
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