Hi I'm Akshay from MSR.
So today I want to talk to
you about FarmBeats projet,
it's a joint project between
MSR India and MSR Redmond.
So there have been
several studies for
the past decade which says by
2015 the agriculture rates needs
to be doubled without any
increasing arable land.
And this demand has to
be met also with very
minimal impact on environment.
So, this brings us to a bigger
broader silence that,
what farm [INAUDIBLE] so, the
goal of farm beats right is to
empower farmers with
data aware farming.
To do that right?
We provide three things.
One, we provide timely
data about the farm or
the crop to various
stakeholders,
second we provide models and
analytics for farms, crops.
And finally automation
systems for irrigation,
pesticide and spraying.
So, [INAUDIBLE] agriculture
right in general is being around
for [INAUDIBLE] various
agricultural researchers have
shown that it improves yield,
reduce cost and
ensures sustainability.
But a key [INAUDIBLE]
is the huge cost for
manual data collection.
If you look at
traditional agriculture,
right, you had a farm land which
had where you put bunch of IoT
sensors, with some wireless
communication be it Bluetooth,
WiFi or
long range communication.
And then send all the data to
the cloud, for processing.
But these has two
main challenges,
one each the farmer right
has very limited resources.
So the cost of these
sensors are expensive and
you cannot put sensors
everywhere in the farm.
Second there is no or
very poor internet connectivity
to send the data to the cloud
for further analysis.
So FarmBeats tackles
these two problems.
FarmBeats tackles these two
problems so we provide an end to
an IoT system for
data driven agriculture.
There are two main inputs.
One, sensor data from
the field and aerial imagery
which is then mashed together to
provide various farm services.
I'm going to talk about
a bunch of farm services
in later slides.
So that is how our system
solves the two problems which
I mentioned.
First, limited resources.
Since we cannot put sensors
everywhere in the farm, right,
we want to work with very
sparse sensor deployments.
But how do we get coverage with
this sparse sensor deployment?
A high idea what we use right
is can we use aerial imagery to
enhance spatial coverage?
So that you put very less
sensors on the field but
use aerial imagery to
cover the entire farm.
One option is to use drones
which can cover large areas
quickly but
it has several challenges,
limited battery lifetime.
And the cost is very high
especially in India right
the regulations for
drone flying is very limited.
So the solution
what we came up was
use low cost aerial imaging
what we call tethered eye.
So we basically use helium
filled balloons where the farmer
walks around the farm to
collect aerial imagery.
So this is the payload which is
attached to the balloon which is
basically a smartphone which
collects images of the farm.
And each the farmer or the user
right has a guidance app
in his hand where he walks by
the farm to collect images.
So the guidance app basically
has an efficient path planning
algorithm so that we cover
the entire region in
an efficient way to
capture most of the data.
So the final system, you have
drones have balloons which
captures aerial imagery and
then generates a panoramic
view of the entire farm.
Couple that with fuel sensors,
which are deployed in the farm.
And then you obtain
precision maps.
So precision maps,
there is basically you have each
point in the farm geocoded
with soil moisture,
soil temperature, other
information which farmers or
the agriculture researchers
are interested in.
Coming to a second problem
right poor interest connection.
So the current systems
right sensors in the field
sending data to a base station.
And this base station sends
data to the farmer's house or
an office nearby and this data
is again sent to the cloud for
further analysis.
But as I mentioned there is very
limited Internet connectivity to
send video or even images,
and sensor data to the cloud.
So a solution what we use
in FarmBeats is Is to use
a gateway device at the farmers
or at the central location which
can aggregate data from various
sensors and provide services for
the farmer locally as opposed
to sending data to the cloud.
So there is very sparse
synchronization with the cloud
but the gateway device should
be able to provide all of
the services to
the farmers locally.
To do that,
there are two main things.
One, we have sensor boxes
that can collect data and
provide information to
farmers in real-time.
So basically, we build
multiple sensor boxes where,
when the farmer walks
around the farm,
can collect data
while he's walking.
So this data is always sent
to the farmers Android app.
We develop these sensor
boxes with multiple wireless
communications, Bluetooth,
Wi-Fi, LoRa for
different scenarios.
So these are generally set up
long term data collection, so
you have a bunch of
sensors in the farm.
These send data to a local base
station and this base station,
based on Internet availability,
sends data to the gateway or
cloud.
The second, so this is sensor
data, but the bigger picture,
the gateway device which
is in the farmer's house or
in a local agriculture center,
right?
Collects data from images, from
the camera, from the sensors,
that's bunch of processing
to provide various services.
So one of the services could
be collect all the images from
the balloons,
generate a panorama image, and
a prediction map, provide
it to the farmer locally.
And the key advantage
of this right
all of the processing happens
locally at the gateway device
without any Internet
connectivity.
When there is Internet
connectivity you could sync with
the cloud and the provide more
services and More services.
So at this point in the entire
project we have this N2 system
which is in place,
from the system right
there are a bunch of services
which we can provide.
If we look at the bottom
most layer right?
You have validator,
video, image sensors.
From that we initially started
out by generating a panorama.
From that obtain a heat map and
correctly combine with
weather forecast right?
We started providing irrigation
scheduling when to put
water and stuff.
That was the initial first thing
we do and then we bounced around
with multiple scenarios for
each of the customers.
One thing which I want to call
out is here in India we have
been working with [INAUDIBLE]
where we primarily focus on oil
production and
biomass production.
There are other bunch
of services which we
are targeting within
these customers.
In terms of deployment,
in India we are carefully not
talking direct to the farmers.
But we are talking to
the agriculture researchers, so
GKVK in Bangalore,
who are the ag researchers who
perform multiple experiments
in the field and
then ultimately disseminate the
best practices to the farmers.
So we are working with them.
So we are working with
them to understand what
are the challenges or what are
the experiments they're doing?
How farm beats can augment some
of the which they are doing?
So currently we have a six
months deployment in GKVK and
where monitoring finger millet,
horse gram and ground nuts.
In US also we have
bunch of deployments.
In US, if you look at it, we
are more focusing on individual
farmers, because there,
each farmer has a large plot,
and kind of controls
the entire farm.
To conclude, FarmBeats can
provide an end to end system for
data collection and
supply data to agriculture.
It acts as a tool to enhance
farming practices and farmer.
And be used by farmers for
applications beyond
precision farming.
Thank you.
>> [APPLAUSE]
>> One quick question before
the next speaker set ups.
>> Yes.
>> What,
>> Yeah AI research having just
a presence.
>> So to stand out
one challenge right?
So currently we are looking
at balloon images for
a particular farm.
To scale out right you can't
use balloons or drones for
each invidual farm
across the globe.
So one of the key issues is can
we combine satellite data which
is available for larger fields
and very sparse balloon or
drone images and combine these
two photos for farm services.
