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Today, I'm here to tell you about the world's food problem.
The world's food production needs to increase by 70% by 2050 to feed the growing population
of the world.
And this is just to feed.
If you talk about nourishing the world, the problem is even more severe.
And the reason this is such a big problem is because the amount of arable land is limited,
the water levels are receding.
So it's like the green evolution problem all over again.
How do we get to this significant increase in food production?
The agricultural scientists have been thinking about this problem for quite a while.
And the most promising approach right now seems to be that of data-driven farming.
What we mean by data-driven farming is the ability to map every farm in the world and
overlay it with lots and lots of data.
For example, what is my soil moisture level, six inches below the soil throughout the farm?
What is my soil nutrient level throughout the farm?
If you could build maps like this, this could enable techniques like precision agriculture.
What we mean by precision agriculture is the ability to do site-specific applications.
For example, right now farmers will apply water uniformly throughout the farm.
They'll apply pesticide uniformly throughout the farm.
With precision agriculture, you could apply it only where it is needed.
Precision agriculture as a technique has been shown to improve yield, reduce cost because
farmers would use less water, less pesticide.
It's also better for the environment because you're not putting in more pesticide than
needed.
You're not putting in more nitrogen, you're not wasting water.
The other technique that such maps could enable is called phenotyping.
Just like you could do genotyping, you could do phenotyping as well.
That is if you could understand why did the same seed variety grow differently in different
parts of the farm?
For example, in the red or blue parts of the farm, you could then create new genotypes.
This map that you're seeing here is one such map that we want to create for all farms
in the world.
In the rest of this talk, I'll talk about precision agriculture, but you can see how
the same techniques apply for phenotyping as well.
So precision agriculture as a technique, given that the benefits are known, was first
proposed back in the 80s.
It's been 30 years since then and the technology hasn't taken off.
The biggest reason this technology hasn't taken off is because of the cost of existing
data-driven agriculture solutions.
Just to give you an idea of how expensive it is, I was at an expo at a university where
there were several companies talking about the latest precision ag equipment, the latest
sensor equipment.
The cheapest sensors that were available there were five sensors for $8,000 on a recurring
cost.
For a farmer to afford that kind of equipment is expecting too much, especially when they
don't know what is the out of life, what is my return of investment, if I buy these sensors
which are so expensive.
That is the goal of the FarmBeats project that I'm leading at Microsoft.
Our goal is to bring down the cost of these data-driven agriculture solutions by two
orders of magnitude.
We want to bring it down from $8,000 to $80,000.
And I'll talk about a few techniques that we think we can help us get there.
Before I go there, I wanted to let you know that I don't have a farming background.
My background is a PhD in computer science.
But the first 18 years growing up, I grew up in India and as it happens in India, people
from India can relate to it, we used to spend, we had three brothers and a sister
and we used to spend time with our grandparents in northern part of India in a small village
in Bihar.
And we used to go there, spend time in farms.
By the way, I did not like farming the fact then.
Those were the worst four months of my life every year.
But the reason was that there was no electricity, no toilets.
It was like going from a city to spend four months in a village wasn't exciting.
And in a way that kind of exposed me to the problems of agriculture.
And that's one of the goals of the Farmbeats Project as well.
We want to take the technologies that we are building to the smallholder farmers everywhere
in the world.
So I'll start by talking about the US, but you can see how this relates to other parts
of the world as well.
So going back, the goal of the Farmbeats Project is to bring down the cost of data-driven agriculture
solutions significantly from where they are.
And I'll talk about three challenges because of which existing solutions are expensive.
The first reason existing solutions are expensive is because of internet connectivity.
The farmer's house in this case has some sort of connectivity to the internet.
They pay for broadband, they get one to three megabits a second.
But the actual farm is a few miles away.
The reason existing solutions are expensive is because the farms don't have connectivity.
They end up using satellite or custom cellular solutions to connect these devices to the
internet.
So how do we bring down the cost of connectivity from the middle of the farm?
To do this, we use one of the prior research I started researching on this concept in 2005
called the TV White Spaces.
What the TV White Spaces enables is imagine if you go buy a Wi-Fi router and plug it in
your house.
Imagine if you could access it a few miles away.
That would be cool, right?
But as soon as you exit your house, the Wi-Fi connection just disappears.
The way we do that is we took a Wi-Fi signal and put it in empty TV channels.
This is over the air TV.
So when you're browsing through over the air TV on certain channels, you see some reception.
Other channels, all you see is white noise.
There's nothing coming there.
With this technology, we were able to fit a Wi-Fi signal in those empty TV channels,
the noisy TV channels, in a way that did not interfere with the reception in an adjacent
channel.
So you could be watching channel seven at home.
On channel eight, we could be sending Wi-Fi signals.
The reason this is so cool is that compared to Wi-Fi at the same power level, in UHF
TV frequencies, your signals go four times farther.
In VHF, they go 12 times farther.
And this is just based on pure physics.
Once you put trees, crops, canopies, and so on, your signals just keep going through.
In our latest experiments, we put these sensors in soil, a meter under soil, and a signal
just keeps going through.
So this was a technology we had built back in 2009, 2010, is when the FCC chairman had
come to visit Microsoft to see the demo we had put together.
This was made legal in the US in 2010.
Since then, we have been deploying this technology in several parts of the world, connecting
rural hospitals, schools, libraries, to the internet using this technology.
Just to recap, this is what the TV white spaces is in Seattle.
Each of these holes there, these gaps, are what is the empty TV spectrum.
And I already told you how it is better than Wi-Fi or any other technology that exists.
In the context of agriculture, our key insight was that TV towers are where people are.
In Rochester, you'll have TV towers.
In New York City, you'll have TV towers.
The farms are away from the cities.
They are fewer people.
So if you go to a farm and turn on and over the year TV, you'll find very few channels.
Most of the channels are just white noise.
The more such empty channels you have, the more capacity you have.
So if you go to a farm, you have a lot of unused spectrum.
We are talking of hundreds of megabits per second of unused capacity.
At which point, we are not only talking of connecting sensors.
You could be connecting cameras, drones, tractors.
You could be getting a lot of information that you previously couldn't get.
If you talk to any agricultural scientist, the number one problem they'll talk about
is data.
How do you get data from the middle of the farm?
With the TV white spaces, we believe we can solve that problem.
Our vision here is just like Wi-Fi connects your house.
This TV white spaces could be used to connect your entire farm.
In fact, this is what we are doing right now in our deployment.
So you put this antenna in and miles around it now gets connected.
You're able to get data that you previously just couldn't gather.
So this was challenge number one.
The second challenge, as I mentioned, what we want to get to are these kind of maps.
What is the soil moisture level?
Six inches below the soil throughout the farm.
How do we get there?
If you wanted to build an accurate map like this, you need lots and lots of sensors.
You'll probably need a sensor every 10 meters.
But putting a sensor every 10 meters is expensive to deploy, to manage.
It'll come in the way of the farmer as the farmer does the day-to-day job.
So the key question is the last bullet over here on this slide.
Can we build such a map using very few sensors?
The way we solve this problem is using UAVs.
These are drones which can fly large areas very quickly.
They have a camera at the bottom that can take images of the entire farm.
The key technology we built using artificial intelligence and machine learning techniques
was a way to use the ADL image to interpolate the data from a few sensors and predict what
these values are in other parts of the farm.
Just to give you an idea, the state of the art, if people had to build maps such as the
one I showed in the previous slide, one would need, one would put a few sensors and then
use either linear interpolation or Kregings method as a state of the art, but just use
that to do the prediction.
With this technology, you can use the ADL image.
The key insight is that if two parts of the farm look similar, either in RGB, hyperspectral
or multispectral imagery, they're likely to have similar values.
So this was one of the key algorithms that we developed.
One, we showed this to work very well for soil moisture, soil temperature and pH.
Once we started talking about it, there were various companies that came to us and told
us to apply this for other services as well.
One of the key challenges in ADL imagery using UAVs, well, we use that in the US, but
as I said, we want to take it to the remotest parts of the world.
Well, UAVs are great, these drones, the ones that you can buy for $1,000.
But $1,000 is still a lot of money if you think of smallholder farmers in Africa, in
India.
Other problems with drones are, for example, in some of the countries where we want to
get ADL imagery.
If we wanted to use a drone, we needed to get permission from the Ministry of Defense.
Well, at that point, it isn't happening.
So then, how do we get ADL imagery at low cost?
The way we do that is we have a low tech solution.
We use tethered helium balloons, which are tethered to the ground.
They fly up to 150 and 200 feet, and they are able to take continuous ADL imagery of the
farm.
They can last up to four to seven days.
The particular thing we built was a custom mount on which you can put your smartphone
and a battery pack.
And this thing can keep clicking pictures for a really long period of time.
There is a farmer in Washington, 25 miles east of Microsoft campus with whom we work.
He uses this technology to monitor floods.
So one of the problems he does, he's a smallholder farmer, he sells his produce to the farmers
market and to restaurants.
One of the problems he runs into is every time there is a flood, he needs to throw away
all his crop, because regulations require that any crop that is touched by the flood
needs to be thrown away.
Right now, that's what he does.
Every morning when he comes, he sees there was a flood, he throws away all his crop.
With this technology, he can monitor.
He knows which crops are actually touched by the flood and only throws away those crops.
In places like India and Africa, someone could just walk around with a balloon or put it
on a bike or a tractor.
And then we have computer vision algorithms based on which we can stitch this together
to create these big aerial maps for the entire park.
The key challenge here was that with drones, you can keep them stable with balloons, they
will move around with wind, your camera is not always facing down.
So we have computer software based on which we are able to solve these problems.
So going back, how do we build those accurate maps?
You'll get the aerial image either from drones or from balloons.
We then build these beautiful pictures of the entire farm.
We then take the raw sensor data and build the machine learning algorithm, the artificial
intelligence algorithm.
That's a model of how would these variables propagate throughout the farm and then use
that to predict what these values are throughout the entire farm.
As I mentioned, we've done this for pH, moisture and temperature and we're working
on other variables as well.
The key takeaway here is that right now, if you look at the startups in the Agri-Culge
AgTech space, a lot of them are working on either sensors or drones.
We believe we are the first who's been able to combine them in a meaningful way, but we
are just starting on this space.
There's a lot more to be done, but this seems like a very promising approach to build these
maps at low cost.
With the TV wide spaces, you can bring down the cost of each sensor.
With this technology, you need much fewer sensors than what you would otherwise need
to build accurate maps like this.
The third challenge is I mentioned how you can gather a lot of data and bring it to the
farmer's house over the TV wide spaces of ply machine learning, but the connectivity
from the farmer's house to the cloud is not that great.
Many farmers, they pay for broadband, but all they get is 1 to 3 megabits per second
connectivity in your house.
Just to give an idea of how limiting that is, if you fly a UAV, a drone, in 15 minutes,
you could be generating over a gigabyte of data.
You can't send that to the cloud over a 1 to 3 megabits per second connection.
It will take a long time.
The other challenge is this connectivity is also prone to outages.
So there's a farmer in eastern New York on the Vermont border at whose farm we've deployed
a system.
Every time there is a snowstorm, there's a high likelihood that his internet connection
goes off.
So in that case, even if we connect the data, but we can't provide this information to
the farmer, it's of no use.
So to solve this problem, what we do, a key insight was that most farmers have PCs.
If they don't have a PC, we ship them a box.
Where everything inside this blue box runs on the farmer's PC.
It takes data from sensors, from drones.
It's able to, over the TV white spaces, it then does a lot of this area-limetry generation.
It does the machine learning piece, the heat map generation, all of that in the farmer's
house itself.
It then we then feed this into the ag services, the blocks and blue are the ones we've already
built, the other ones are the ones we're working on.
Of course, as I mentioned, my background is not in agriculture, so we partner with people
who understand agriculture, the agronomists, farmers, to build all those services.
The other thing is that a lot of data stays in the farmer's house.
We're not sending all the data to the cloud.
For example, the detailed drone imagery, the gigabytes of video, you can ship all of that
to the cloud, but we transcoded, compressed and compressed version to the cloud.
Because these are some of the unique features on the left of the entire system, it can also
run offline.
We can disconnect the system from the internet and it continues to run.
So just to give you an idea of how farmers are using it, we've deployed it in quite a
few farms now.
I'll talk about two of them.
One is a small farm, close to Microsoft campus, the smallholder farmer, and the other is a
2,000 acre farm in upstate New York.
The kind of insights we can provide to the farmers are, I'll walk you through some of
them.
So, for example, the four kilometer stretch, the farmer in New York, he wanted to know
how his cows are doing.
We flew the drone and within 30 minutes we transferred the data with the TV white spaces
to this PC and we can start generating insights like the grass is growing back well from left
to right.
There is a water puddle that needs to be fixed before the next planting season.
The cows are pooping well, which is also important information for the farmer.
This is where the cows are and this is a stray cow that needs to be herded in.
All of this within 30 minutes of flying the drone.
The state of the art is people that fly the drone, take the SD card out, go to a city,
upload all the data, wait for 24 hours.
By that time, the stray cow would have gone somewhere else.
With this, we can generate all of this within 30 minutes.
Other kind of insights, this is the farm incarnation.
We are able to show the farmer beautiful pictures like this.
This is a soil moisture map where we were able to flag that the top left corner of the
farm is still moist.
Even though we did not have a sensor over there, this is after the farmer had applied
lime, we were able to flag that the dark parts that you see here are still acidic.
The key question you would ask is how accurate is this?
We went to three farms, three different five acre plots and we captured a thousand measurements
and we asked the question that if we pick just 10 of these, how accurately can we predict
the 990 other values using the farm beads technique of using aerial imagery with ground
sensor data.
These are the results for temperature, pH and moisture.
This is how accurate farm beads is and this is how accurate the actual sensors were.
That is, the sensors for example we used for soil temperature would report temperature
in one degree Fahrenheit and so on.
So the key takeaway here is not that we are more or less accurate than the actual sensors
themselves, but that our predictions are so close to the actual measurements that they
are actually actionable by the farmer.
Then again another scenario was we had cameras in barn streaming data with the TV white spaces
where we were able to flag cows or the cows are moving around well where the some cow
is sick, all of this because we are doing the TV white spaces and we have a PC where we
are running all the analysis in the farm itself.
So to conclude that is what farm beads is.
I wanted to make you aware of the food problem and some of the steps that we are taking,
even not being in the agriculture space, of how we can help solve some of the food problem.
With the TV white spaces we are able to gather a lot of data that didn't exist and applying
the latest advances in artificial intelligence and machine learning we can bring actionable
insights to the farmers.
So these are the two farmers we work very closely with, the three farmers Sean, Mark and Kristen,
they are just amazing people.
So with that I wanted to end the talk.
Thank you.