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CRAIG GANSSLE:
Today, we're going
to talk about how artificial
intelligence and how
my company is working
with Google to help
power global food production.
My name is Craig Ganssle.
And I'm the founder and
CEO of a company called
CAMP3 out of Atlanta, Georgia.
We have a product
called FARMWAVE.
Artificial intelligence
can play a huge role
in important industries, such
as global food production,
just to name a few.
If I were to ask
you, "What is this?"
You would tell me it's an apple.
But what if I said,
now prove it to me?
Prove to me that
this is an apple.
How do you know that?
Because when we were kids,
preschool or kindergarten,
we all saw the same
thing, the little
flashcard with the picture.
And it said apple.
What about that?
That's an apple too.
But it doesn't look like that.
So what do you
mean it's an apple?
What about that one?
That looks different too.
Or that?
These are all apples.
But in global food production,
using artificial intelligence,
we have to prove it.
And not only would
we have to prove it,
that it's an apple, but
all the pathogen, pest,
and weed infestations that
could infect any crop out there.
Welcome to the challenges
of AI in plant pathology
in agriculture.
It's a daunting task to use
things like image recognition
to identify pathogen and
pest infestation in crops
or plants--
tomatoes, citrus, row
crops, anything-- early on.
But this is one of the
most critical stages
in growing food.
If we can identify pathogen and
pest infestation early enough,
that means we could
spray less chemicals.
That means we have less crop
destruction, bigger crop
yields.
That could mean more food.
This is an industry that is
really important globally
because we all need to eat.
Dr. Fei-Fei Li has said it best.
Build a new dataset.
And so at FARMWAVE, that's
what we set out to do.
We have searched all
around the world--
universities, large stakeholder
corporations, farmers,
smallholders-- to
capture imagery and build
a dataset that is
curated and cataloged
for image recognition
for plant pathology
and reducing crop loss.
FARMWAVE is an app that
connects people in agriculture
with their farm and
technology and each other.
It's more than just the
artificial intelligence,
but that's the real
powerhouse behind it.
In the past couple years that
we've been developing FARMWAVE,
we've done pilots and proof
of concepts around the world--
with large corporations, such
as Monsanto, Syngenta, Bayer,
small farmers from India,
France, the United States,
all around the world--
and found out that if
we could get FARMWAVE
into the hands of farmers
all around the world,
we have a really
good shot at reducing
crop destruction by 20% to 30%.
One of the components is
communities, enabling farmers
to talk to each other.
Today, they use things
like Twitter and Instagram.
But in their day-to-day
job, and how fast they move,
and all the tasks that
have to happen on the farm,
they're using Twitter
and Instagram.
But all the noise of the news
and everything else they follow
is kind of getting
stuck in the middle--
not to mention you
have some growers who
grow for large
stakeholder companies
where they're
doing plant trials,
and that information is
proprietary and shouldn't
be out on the public web.
FARMWAVE gives them a
closed-loop community
where researchers,
agronomists, and farmers alike
can collaborate
by building field
reports in near real-time.
This allows us to
have the data right
in the palm of their hands.
There's a lot happening
in agriculture today.
All around the world,
there's different companies
that are making attempts
at autonomous vehicles.
John Deere's been doing
it for a very long time.
We've had autonomous
tractors for decades.
They move a lot slower, and
there's nobody in their way.
But they've been
doing it for a while.
Unmanned aerial vehicles,
satellite imagery,
hyperspectral color
imagery, all of this data
is very important
to agriculture.
And there are many,
many companies
around the world trying to make
an impact into how we could
better make use of
all of this data
to make better decisions
in our farming.
Howard Buffett wrote
the book "40 Chances."
If you think about it, a
farmer has in their lifetime
an average of 40
chances to get it right.
And that's it.
Because everything that
they do, their day-to-day,
is very seasonal.
Everything plants in the
spring, they harvest and get
the results in the fall.
Whatever didn't work, they
can try it again next year.
A farmer in Indiana,
Chris Tom of Tom Farms,
they farm about 45,000 acres.
They're one of the largest
growers in the United States.
He said something to me once.
Don't add more technology on
top of existing technology
for the sake of
adding new technology.
So we're trying to train
things from the ground
up with FARMWAVE and our
artificial intelligence
to work with the devices that
they're already carrying around
with them or that they're
already using today,
such as unmanned aerial vehicles
and even some of the machinery.
We leverage a lot
of technologies
within Google Cloud Platform.
Recently, we've been one of
the alpha companies working
with AutoML.
And we have a partnership
with the University of Georgia
agriculture extension-- which
is where we are from, Atlanta--
where we had what is called the
Consortium for Internet Imagery
Database System.
Owned by University of Georgia,
and about 19 other universities
around the world
have participated in,
it is 20 years of imagery data
on over 35,000 plant species.
So we've worked closely with
the University of Georgia.
And we got a copy
of this database.
And we started
classifying, and labeling,
and tagging, and running
imagery through FARMWAVE.
It's been very,
very challenging.
20, 19, 18 years ago, we didn't
have the kind of mobile phones
that we have today.
So the pictures
really aren't usable.
They're not a very good quality.
But as we started to
see more recent imagery,
we have better quality.
Some are good pictures.
Some are not.
In plant pathology, you have
to get up really, really
close and get a really
good picture of a leaf
to get really early
detection where you'll
have an impact that you can
identify a pathogen or a pest,
make a recommendation
and a treatment,
and put the input in the field.
We also got early on
into counting sequences.
Today, farmers will
help predict yield
by counting the kernels
on an ear of corn.
They do that with a Sharpie.
Now, I don't know how many of
you have spent time farming.
But if you are in the
south Georgia heat in July
with a Sharpie, and you're
trying to count kernels
on an ear of corn, it's awful.
And by the time
you roll it around,
some of the sweat in your
hands washed off the marker.
And you don't really have
a good, accurate count.
So using some of the
Vision API technology,
we've developed a
system that allows
you to take a picture of the ear
of corn, rotate it 180 degrees,
take another picture.
And it counts it in
about two seconds.
We've been 95-plus percent
accurate on these counts
consecutively-- indoors,
outdoors, regardless
of different light levels.
This is an increase
by 17% to 20%
of what was acceptable,
according to the Iowa Corn
Board.
Later this year,
we're going to be
launching what's in
the middle image, which
is called Stand Counts.
Stand Counts allow farmers
to know how many they
have in a given row.
Sometimes, this imagery
can be taken by UAVs.
Sometimes it's
taken on the ground.
Cluster counts in
tomatoes, wine grapes
helping to predict yield.
The more decisions that
farmers can make earlier on,
the better it is for overall
global food production.
The image recognition
for us was about time.
Now, let me give you an idea
of what it would start like.
Here's how it's
typically done today.
A farmer goes out
into the field.
Or, satellite takes a picture.
Or, a drone takes a picture.
And they see that
there's a distress
in an area of the field.
Then, they'll send somebody
out, and they'll take a picture.
Maybe the farmer
knows what it is.
Maybe they don't.
Because even today, we still
see new diseases that pop up.
So they say, OK, I don't
really know what this is.
I've got to get this
to an agronomist.
But I live out in
the middle of nowhere
in Iowa in a sea of corn.
The nearest university
is four hours away.
So I make a phone call
to my local agronomist.
He's two states away.
He'll get to me maybe next week.
So he reaches out
to the university.
They say, we can get
somebody to come out and take
a look in a couple of days.
So they come out.
They take a look.
If they know what it is,
they make a recommendation.
They write a prescription.
And the farmer
calls their dealer.
And they come out and put down
either an herbicide, fungicide,
pesticide, whatever it might be.
If they don't know
what it is, they
have to do some
further research.
Maybe that takes a couple
more days, maybe another week.
Depending on climate, weather,
traits, whatever they have,
this could spread.
This could spread from
5% of their crop to 20%
to 30% to 40%, decimating
their yield, losing food.
For us, it's about time.
FARMWAVE and the image
recognition capability
makes this happen in
a matter of seconds.
Now, 35,000 species
from the University
of Georgia, 20 years of crop
imagery, we are just getting
started.
This is a huge database.
Corn alone and all
the diseases in corn
has taken us several years just
to kind of get under our belt.
Because if you remember the
pictures from the apples,
we have to look at a crop in
all of its different stages
and every disease for that crop
in all of its different stages
from early on to 20% severe,
60% severe, fully decimated
to get accurate results.
Our library of imagery
is one of the only one
we know of today
that we have been
told that has been
properly validated
by PhD level pathologists
and entomologists
from around the world.
But we have to
have high accuracy.
So we have to have that.
But it takes more time.
When farmers go out to a field--
if you've been through farmland,
you'll see these placards.
It tells us what kind of crop
is being planted right there.
In this case, it's
a Pioneer version.
Using the Google
Vision API, farmers
are able to very quickly
fill out their field report
by simply taking a
picture, extracting
the information
from the placard,
and putting it into
the field report.
Then they continue that field
report when they get out
of their vehicle, and they go
into the field to actually look
at a pathogen or pest problem.
This saves a lot of time.
Maybe it's just a few
minutes a day for a farmer.
But that compounds for farmers
around the world every year.
This sort of
information is critical.
It has to be filed with state
regulatory and local county
reports.
So this information is
important to get right.
The Google Vision API has
helped us tremendously
in extracting this
information very quickly when
going to the field.
This is a little bit
of a look at a field
report inside FARMWAVE.
We help them by recognizing
the pathogens and pests.
But we leverage everything
in Google Cloud,
where FARMWAVE lives, to get
information in near real-time.
We also geotag where they are.
It's very important.
When they go out
to a field, what
has happened in this field?
We tag weather, what the
weather was, where they are,
time, and date.
The future of
FARMWAVE next year is
to launch an app store in 2019
that allows other machinery
and data points from
all within agriculture
to connect to FARMWAVE so
we can correlate that data
and collaborate better.
That means if a farmer
goes back out to a field
and opens up FARMWAVE
to take a field report,
it sees where they are.
And it says, let me pull
your John Deere planter
because I know where you are.
And I will tell you
in a few seconds what
was planted in this field,
on what date, what seed, what
variety, at what spacing,
at what compaction.
And then let me pull your
irrigation sensor data.
And I will tell you how
much water this crop
has had from the day
it's gone into the ground
until this very moment based off
exactly where you're standing.
It pushes those results.
It puts them in
the field report.
Then you can take pictures,
notes, video, dictation
and save it.
And when you save it,
in those communities.
I mentioned earlier, it
gets shared right away
with your agronomists,
researchers, growers, dealers
anywhere in the world
in near real-time.
A few years ago,
I had the pleasure
of visiting with President
Emmanuel Macron of France.
France is the capital of the
world in wine and cheese.
And it was fantastic to see
how they grow differently
in France than they do
here in the United States.
And I've been to Amman,
Jordan, and Beirut, Lebanon,
and how they grow differently
there than they do in France
than they do in
the United States.
France is one of those countries
that GMOs are prohibited.
So they have different styles.
But data is still as important.
And farmers all around
France work together
through subsidies
from the government
for new technology every year.
We've been visiting with
France the past couple years
several times, working with
different co-ops, growers,
and even with the government on
how FARMWAVE could potentially
be the single platform that the
country uses to aggregate data
and do predictive
modeling in the future.
Because as we look
at AI, and as we
look at machine learning
and deep learning
and what we can leverage
Google Cloud Platform to do,
it's about those 40 chances that
Howard Buffett talked about.
It's about, what
can we do and learn
for the future based off the
knowledge that we get today?
I just had a conversation
recently with a lady,
Dr. Suzanne Wainwright-Evans.
She's a world-renowned
entomologist, Buglady.
She said, I'm pretty skeptical
about FARMWAVE, Craig.
I come from decades
of experience.
And that experience and
what I do in the field,
we call biases in AI.
And she's absolutely right.
There's a lot of biases.
There's a lot of things that
entomologists will just see
and do just as their nature from
being an entomologist for years
that are going to be really hard
for us to train in the system.
And we're working
very hard on that.
But the fact is Dr.
Suzanne Wainwright-Evans
will not be here forever.
And today, it's not very
popular to be the next Buglady.
So we don't have as
many entomologists.
In 2017 alone, Dr. Evans
spent three weeks at home.
The rest was on the road.
She is extremely sought after
in other countries, where
they see pest infestations
that they don't even
know what it is.
And they need her
help to save crops.
So working with
somebody like Dr. Evans
to help train our machines,
to help train our AI
to know what we're looking at
in pathogen or pest infestation,
is critical to preserve her
knowledge of the past couple
decades for the
future of agriculture
tomorrow so we can
better make decisions.
These are some of the
partners that we work with.
Obviously, I talked about
the University of Georgia.
We did some early work
with Google X. FARMWAVE
actually began on Google Glass.
I was an early adopter
of Google Glass.
I went up to New York.
I was asked to come up there
and get a pair of Google Glass.
And a few months
after I got it, I
went to a John Deere developer
conference in Des Moines, Iowa.
And John Deere had
invited their developers
from all around the world to
come in for this conference.
And it was kind of a, what do
you have that's new for us?
And the developers were sort
of looking at John Deere,
saying, well, what do you
have that's new for us?
This one gentleman
came up to me.
And he said, Craig, would you
get up and talk about Google
Glass and what it's doing?
Because a lot of people have
asked us about it and what
it is.
I said, sure.
At this point in time,
FARMWAVE wasn't even born yet.
We had just gotten up and
talked about Glass and what
it was doing out of the box.
And this was with
the Explorer edition.
And then as I learned
a little bit more
about the critical problem
solving in agriculture,
and we learned about the
operation of a crop scout
to identify these
things, I knew that we
could use image
recognition technology
to make these problems
go away and make
these decisions faster.
So it actually began
on Google Glass.
We did a pilot with
Monsanto in 2015
on about 25 different pairs
of Google Glass in Illinois,
where farmers were able
to go out to the field
and simply use their
voice and take pictures.
And it would identify.
Or, they would have the
hands-free option because
of Glass to hold it up
and count the kernels
in a matter of seconds.
So that's some of
the work that we
did early on with Google X. It
still does work today on Glass.
And actually, yesterday, we
launched FARMWAVE globally
in the iOS and
Android app stores.
I was at an ag tech conference
in Atlanta yesterday.
And we did our global launch.
And we've got 80
countries and about
34,000 people in
the past 60 days
who have signed up to
early adopt FARMWAVE.
It's a critical problem.
We are expected by 2050,
for the amount of people
that will be on
the planet, we have
to grow more food
in the next 30 years
than we have in the last 8,000--
with less resources, less
land, and more mouths to feed.
Technology is going
to play a pivotal role
in making this happen.
And we're really glad
to be part of that.
And we've not been able to do it
without the powerful resources
of Google Cloud.
Within the image recognition
that you see on this report 01,
you'll see that there's
a map at the bottom.
I also wanted to point out that
the geospatial capability, when
you take a field report
to show where you are,
that populates on a map.
Now, if I'm in Iowa,
and I'm growing corn,
and I see that I have
a problem, and I'm
in one of these communities
with the Iowa Corn Growers
Association, and
I take a picture,
and I do a field report, and I
alert my neighboring farmers,
they'll see where a
disease is actually
spreading in near real-time.
And through a
dashboard view, if you
are an administrator of
your FARMWAVE community
or your FARMWAVE
group, you can actually
see all of the growers.
And through different tags and
different tagging on the map,
you can actually see it grow
and spread in real-time.
This has an alert
effect that can
alert other farmers
of what's happening
and what's coming their way.
And it allows, in that
community, for those farmers
to communicate with others
around the world for advice.
A dairy farmer in Wisconsin
who farms about 860 cows a day,
three times a day, is pretty
advanced in what he's doing.
But a dairy farmer in India
who only has eight cows
doesn't have the knowledge or
the resources or the skillset
that the gentleman
in Wisconsin does.
Through FARMWAVE, he can
send pictures and ask advice.
And because those pictures
are in a field report--
that is, location- and
weather-tagged-- the farmer
in Wisconsin can immediately
advise the farmer in India.
I see that you have eight cows.
You live in a very
humid climate.
Here's the conditions
that you're dealing with.
That's very likely mastitis.
And here's what you
need to do about it.
And that's critical to
farmers in other parts
of the world in
growing countries
where those eight cows is the
sustainability of their family.
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