>> Hey, I'm Scott Hanselman and it's
another episode of Azure Friday.
I'm going to be talking with Dr. Ranveer Chandra and
his team about how they're taking years and years
of research from Microsoft Research and
putting into the hands of farmers in agribusiness,
to make food better for everyone.
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>> Hey friends, I'm Scott Hanselman and it's Azure Friday.
I'm here at Microsoft Ignite in Orlando with Dr. Ranveer Chandra,
and you have a lot of really cool equipment here.
This is going to be the best booth of any booth at Ignite.
>> Thank you. It is.
We've got a tractor, we've got an entire farm,
and we're showing how you enable
data-driven agriculture with FarmBeats.
>> Data-driven agriculture.
How do people do agriculture today?
They just feel this ground is dry, I need to water it.
>> Yeah. So a lot of farming is based on guesswork.
Most of the decisions that a farmer takes about when to water,
when to apply pesticides,
when to apply nutrients,
fertilizer is based on guesswork.
We believe that instead of using guesswork,
if a farmer uses the knowledge coupled with data,
and data-driven insights, we can make agriculture more productive,
more cost-efficient, and it's also better for the environment.
That's what we mean by data-driven agriculture.
>> Okay. So where does this data come from though?
I mean, I can buy a little stick that tells me
the pH and I can check for nitrites and stuff,
but if I really want data about my soil, what do I do?
>> Yeah. So this data that people talk about,
one of the ways is to get sensors in the farm.
Like some of these sensors that our partners make,
this is made by Teralytic.
This is made by Davis Instruments.
These sensors can go inside soil and gather large amounts of data.
For example, this particular sensor
has about 26 different types of sensors.
It measures nutrients, it measures moisture.
This thing here from Davis it measures wind, humidity.
A lot parameters about what's
happening in the farm at that point, at that location.
One of the challenges, of course as well,
the sensors they need to communicate.
They need to send the data to the Cloud to make sense of it.
Most of these farms are disconnected.
They are far away from the cities,
they don't have any Internet access because of which,
existing solutions quickly become expensive.
To solve this problem, one of the innovations we have in
FarmBeats is what we call the TV white spaces.
Like what you're seeing this antenna here, you remember this?
You used to watch TV with these antennas.
>> Yeah, UHF.
>> Exactly. You can used to flip through TV,
on certain channels you saw a transmission,
other channels all you saw is white noise.
What we've been doing in research for but about 14 years now
is developing a way where in those noisy channels,
you can put WiFi signals.
The advantage is compared to WiFi at the same power level,
in UHF TV channels,
your signals go four times further.
>> So you're tunneling data transmissions inside of the static,
in the in-between spaces, between channels.
>> Exactly. Because these channels
are lower in the frequency domain,
your signals go really far.
The interesting thing about agriculture is
that the TV towers are in cities where people are.
If you go to farms,
most of these channels are just white noise.
The more white noise channels there are,
the more unused capacity there is.
So you can then start sending data from these different sensors,
all the way to Azure, to the Cloud,
where we can start aggregating a lot of this data from the farm.
>> Right. Because WiFi would be like in the gigahertz range,
two or five gigahertz.
We're talking about the low megahertz, big sweeping waves.
>> Exactly. Yeah.
>> That can go very long distances, that's brilliant.
Okay, so I've got the sensors but I want to get imagery.
I want more pictures, I might want detail.
What's going on behind you here?
>> Yeah, the interesting thing is sensors would
only tell you what's happening at that location.
It won't tell you what's happening 10 meters away.
So if for example,
the problem was what is
the soil moisture level
six inches below the soil throughout the farm?
You would need a sensor every 10 meters.
But putting one of those sensors
every 10 meters is expensive to deploy, to manage.
It'll come in the way of the farmer
as the farmer does day to day job.
So the key technique we built was a way to combine
ground sensor data with
aerial imagery so that with very few sensors,
and with aerial imagery from say drones or robots, or satellites.
You can then train a machine learning model to then
start making predictions in other parts of the farm,
where you do not have sensors.
What we showed was using this technique where you combine
ground sensor data with aerial imagery from drones,
robots, or even from satellites.
You can then build these maps which are three times more
accurate than if you did not use this aerial imagery.
>> Interesting.
>> So here what you're seeing is this is a drone from DJI,
which is for other partners.
This is a fixed-wing drone from senseFly.
Then what you're seeing is an earth sense robot.
This thing goes between rows.
This has multiple cameras,
this has a Lidar sensor as well.
This is another camera SlantRange,
which gets frequencies in different parts,
which gets images in
different parts of the spectrum, optical images,
which you can then analyze and get information about crop stress,
soil stress, and different things,
which you just couldn't get until now.
>> It's interesting that you point that out because you're using
the electromagnetic spectrum in
a creative way by tunneling data there.
You're not just taking visible light,
you're using much of the spectrum that would be appropriate.
>> That's right.
>> That's way it's okay. So I've collected all this data,
presumably terabytes of data over hundreds of acres,
what do I do with it then?
>> Yeah. So all of this data then goes to the Cloud.
What we built with Azure is
Azure Data Hub where all of
these data is put in one place in the Cloud,
and then we apply AI and machine learning on top of
this data to combine
these different data streams to start adding value.
Like what you're seeing here,
this is one of the dashboards.
This comes as a solution accelerator with our FarmBeats offering.
In this dashboard, this is actually a real farm.
>> Really?
>> This is Andrew Nelson's farm in Eastern Washington.
He's a fifth generation wheat farmer,
and in his farm what you're seeing is this is in his entire farm,
we divide them into fields.
Here what you're seeing is for each field, this is, for example,
telling you how the soil moisture is varying throughout the field.
We combined this with sensor data to build these indices.
EVI is the vegetation index,
which tells you how this crop is doing, whether they're in stress.
This is NDWI, which tells you the water index,
how the water levels are varying in the farm.
The way we build this is by combining data
from sensors, from satellites,
from the drone imagery which all comes to the Cloud,
where we then merge all of this and
present all of these insights to the farmer.
>> So how does he take those insights and say,
"This is meaningful to me.
There's something happening here," and then take action on it?
>> Yeah. So one of the things we do is we'd
identify parts of the farm that are under stress,
that don't look normal,
using things like anomaly detection and
use that to flag this information to a grower.
>> Wow. So how does someone get this information,
is this something that costs millions and millions of dollars or
where do I find this data and this solution?
>> This solution, the way we are taking this to
market is as an Azure Marketplace offering.
So FarmBeats is now available on Azure Marketplace.
Anyone can go there, installed it.
It would run in your subscription,
in the customer's subscription.
>> Oh, so it's not FarmBeats as a service,
it's running in your Azure subscription?
>> Yeah. So the data, we don't own the data.
The data is going in
the customer's subscription with all of this data,
they can then go there and sign up
for our partners: partner sensors,
partner drone companies, partner data.
Then FarmBeats is not built as a farmer facing offering,
it's not meant for the growers.
It's meant for partners to build solutions on top.
We provide these insights but we want partners,
ISPs, to build solutions on top of this for all the growers.
We are not an agricultural company.
>> No, that makes sense.
>> Yeah, but we are partnering with
the agriculture companies to build these solutions to farmers.
>> So then if I were a fertilizer company and I was interested
in selling fertilizer in the most effective way,
then I might go and present that to a farm and say,
"I can help you use your fertilizer more efficiently.
I'll install these sensors, no cost."
Then they get the benefit without
the farm having to run an Azure subscription.
>> Exactly.
>> That's fantastic.
>> Yeah.
>> So if people go out and search for Azure FarmBeats,
they can learn all about this.
Can they make a demo farm if I just want to do one on my own?
>> Yeah. So you can go right now, install FarmBeats,
get the satellite data and get the initial maps.
To get additional insights, you'd add sensors.
To get more detailed insights, you'd add drones.
We'll continue to add more and more partners to the ecosystem,
to start bringing more insights to that particular farm.
Our vision with all of this, as I said,
is to enable data-driven agriculture,
but it's also to make it more affordable.
That is, we want these solutions so
that every farmer in the world can start
getting data and start using
this data or this data-driven insights and towards that,
we'll keep pushing the boundary of adding more partners,
innovating on different things you could do on top,
so that everyone can start benefiting from
data and from the insights
that can be generated on top of this data.
>> Fantastic. Thank you very much, Dr. Chandra.
>> Thank you, Scott.
>> I learned all about Azure FarmBeats here at
Microsoft Ignite in Orlando and this is Azure Friday.
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