How data-driven farming could transform agriculture | Ranveer Chandra | TEDxUniversityofRochester
Summary
TLDRThe speaker addresses the critical issue of increasing global food production by 70% by 2050 to feed the growing population. They highlight the challenges of limited arable land and receding water levels, proposing data-driven farming and precision agriculture as solutions. By using technologies like TV white spaces, UAVs, and machine learning, the FarmBeats project aims to reduce the cost of data-driven agriculture, making it accessible to smallholder farmers worldwide. The goal is to improve yields, reduce costs, and benefit the environment by enabling precise, site-specific applications of resources like water and pesticides.
Takeaways
- 🌱 The world's food production needs to increase by 70% by 2050 to feed the growing population, highlighting the urgency of the global food problem.
- 📈 Data-driven farming is a promising approach to significantly increase food production by leveraging data to optimize farming practices.
- 🚜 Precision agriculture, which involves site-specific applications of water and pesticides, can improve yield, reduce costs, and benefit the environment.
- 🌡️ Mapping soil moisture and nutrient levels throughout a farm can enable more precise farming techniques and is crucial for data-driven farming.
- 💡 The Farm Beats project at Microsoft aims to reduce the cost of data-driven agriculture solutions by two orders of magnitude, making it more accessible to farmers.
- 📶 Internet connectivity in farms is often expensive and limited, hindering the adoption of data-driven farming technologies.
- 📡 TV white spaces technology can provide cost-effective connectivity in rural areas, enabling the use of data-driven farming solutions.
- 🤖 Using UAVs (drones) and machine learning, it's possible to create detailed maps of farms with fewer sensors, reducing the cost of data collection.
- 🎈 Tethered helium balloons with cameras can be a low-cost alternative to drones for capturing aerial imagery of farms.
- 🖥️ Local processing of data on a farmer's PC can overcome connectivity issues and provide real-time insights without the need for internet.
- 🔢 Farm Beats has demonstrated high accuracy in predicting soil conditions, which can be as reliable as direct sensor measurements for decision-making.
Q & A
What is the projected increase in world food production needed by 2050 to feed the growing population?
-The world's food production needs to increase by 70% by 2050 to feed the growing population.
Why is the increase in food production necessary not just to feed but also to nourish the world's population?
-The necessity to nourish the world's population makes the problem of increasing food production even more severe due to limited arable land and receding water levels.
What is data-driven farming and how does it relate to solving the world's food problem?
-Data-driven farming refers to the ability to map every farm in the world and overlay it with various data, such as soil moisture and nutrient levels. This approach can enable techniques like precision agriculture, potentially increasing food production.
What is precision agriculture and how does it benefit farming practices?
-Precision agriculture is the ability to apply water, pesticides, and other inputs uniformly or site-specifically only where it is needed. It improves yield, reduces costs by using fewer resources, and is environmentally friendly by avoiding overuse of pesticides and water.
What is phenotyping in agriculture and how does it differ from genotyping?
-Phenotyping in agriculture is the process of understanding why the same seed variety grows differently in different parts of a farm. Unlike genotyping, which focuses on the genetic makeup of plants, phenotyping observes the physical characteristics and growth patterns influenced by environmental factors.
What is the Farm Beats project and what is its main goal?
-The Farm Beats project, led by Microsoft, aims to significantly reduce the cost of data-driven agriculture solutions by two orders of magnitude, making it more accessible to farmers and potentially revolutionizing farming practices.
Why has the adoption of precision agriculture technology been slow despite its benefits?
-The slow adoption of precision agriculture technology is primarily due to the high cost of existing data-driven agriculture solutions, which can be prohibitive for many farmers.
What is the TV white spaces technology and how does it benefit agriculture?
-TV white spaces technology utilizes unused TV spectrum to provide long-range Wi-Fi-like connectivity. In agriculture, it can be used to connect remote farms to the internet, enabling the transmission of data from sensors and other devices.
How does the use of UAVs or drones contribute to creating accurate farm maps?
-UAVs or drones equipped with cameras can quickly cover large areas of a farm and take images. Combined with artificial intelligence and machine learning, these images can be used to interpolate data from a few sensors and predict soil conditions across the entire farm.
What is the challenge with using drones for aerial imagery in remote parts of the world, and how is it being addressed?
-The cost of drones and regulatory restrictions can limit their use in remote areas. To address this, tethered helium balloons with mounted smartphones are used to capture aerial imagery at a lower cost and without the need for special permissions.
How does the Farm Beats project handle the issue of limited internet connectivity from the farmer's house to the cloud?
-The project uses a PC-based system that processes data from sensors and drones locally, reducing the need for high-speed internet. It also compresses and sends only necessary data to the cloud, ensuring that the system can run even with limited connectivity.
What kind of actionable insights can the Farm Beats system provide to farmers?
-The Farm Beats system can provide insights such as soil moisture levels, pH levels, and the growth patterns of crops. It can also monitor animal health and movement, and flag areas that require attention, such as water puddles or acidic soil patches.
How accurate are the predictions made by the Farm Beats system compared to actual sensor measurements?
-The predictions made by the Farm Beats system are so close to the actual sensor measurements that they are considered actionable by farmers, providing reliable data for making informed decisions.
Outlines
🌱 Addressing the Global Food Production Challenge
The speaker begins by highlighting the urgent need to increase the world's food production by 70% by 2050 to accommodate the growing population. The challenge is exacerbated by limited arable land and receding water levels, reminiscent of the Green Revolution. The speaker introduces data-driven farming as a promising solution, which involves mapping farms and overlaying them with data like soil moisture and nutrient levels to enable precision agriculture. Precision agriculture is shown to improve yields, reduce costs, and benefit the environment by applying water and pesticides only where needed. The talk also touches on phenotyping, which could lead to the creation of new genotypes. The speaker's background in computer science and personal experiences in India with agriculture set the stage for the Farm Beats project at Microsoft, aimed at significantly reducing the cost of data-driven agriculture solutions.
📡 Leveraging TV White Spaces for Farm Connectivity
The speaker discusses the high costs associated with existing data-driven agriculture solutions, particularly the internet connectivity required for remote farms. To address this, the speaker introduces the concept of TV white spaces, which are unused TV channels that can transmit Wi-Fi signals over long distances without interference. This technology, once legalized in 2010, has been used to connect rural areas to the internet. The speaker's insight is that farms, often located away from cities, have many empty TV channels, providing ample unused spectrum for connectivity. The vision is to use TV white spaces to connect entire farms, enabling the collection of data that was previously unattainable. The speaker also explains how this technology can be used to connect various devices on a farm, facilitating the gathering of extensive agricultural data.
🚁 Utilizing UAVs and Balloons for Aerial Farm Imaging
This section delves into the challenges of creating detailed maps of farms to understand soil conditions and other variables. The speaker proposes the use of Unmanned Aerial Vehicles (UAVs) or drones to quickly cover large areas and take images, which can then be used with artificial intelligence and machine learning to interpolate data from a few sensors across the entire farm. The speaker also addresses the challenges of using drones in certain countries and introduces a low-cost alternative: tethered helium balloons equipped with smartphones to capture continuous imagery. This approach allows for the creation of detailed maps with fewer sensors and provides a solution for areas where drones are restricted or too costly. The speaker describes how this technology has been used to monitor crops and prevent waste due to flood damage, with the potential for adaptation in various parts of the world.
🖥 On-Farm Processing and Machine Learning for Precision Farming
The speaker addresses the issue of limited internet connectivity from farms to the cloud, which hinders the transfer of large amounts of data collected by drones and sensors. To overcome this, the Farm Beats project utilizes on-farm processing, where a PC or a provided box runs analyses using data from sensors and drones, applying machine learning to generate actionable insights without relying on cloud connectivity. The speaker emphasizes that this approach allows for the retention of much data on the farm itself, reducing the need to send large volumes of data to the cloud. The system can also operate offline, ensuring continuity even during internet outages. The speaker shares examples of how this technology has been deployed and used by farmers to gain quick and valuable insights into their operations, such as monitoring cow health and identifying areas of the farm that require attention.
📊 Demonstrating the Accuracy and Impact of Farm Beats
In the final paragraph, the speaker presents the results of accuracy tests for the Farm Beats system, comparing its predictions for soil temperature, pH, and moisture with actual sensor measurements. The findings show that the predictions are highly accurate and actionable for farmers. The speaker also shares specific examples of how the system has been used to provide real-time insights, such as identifying areas of a farm that need attention before the next planting season and monitoring cow health. The speaker concludes by expressing gratitude for the opportunity to address the global food problem and showcasing the potential of technology to assist in solving it, highlighting the collaboration with farmers and agronomists to develop and implement these solutions.
Mindmap
Keywords
💡Food Production
💡Arable Land
💡Data-Driven Farming
💡Precision Agriculture
💡Phenotyping
💡TV White Spaces
💡UAVs (Drones)
💡Artificial Intelligence (AI)
💡Machine Learning
💡Soil Moisture
💡Cost Reduction
Highlights
World's food production needs to increase by 70% by 2050 to feed the growing population.
Agricultural scientists are focusing on data-driven farming to address the food production challenge.
Data-driven farming involves mapping farms and overlaying them with data like soil moisture and nutrient levels.
Precision agriculture allows for site-specific applications of water and pesticides, improving yield and reducing costs.
Phenotyping can help understand why the same seed variety grows differently in different farm areas.
The Farm Beats project at Microsoft aims to reduce the cost of data-driven agriculture solutions by two orders of magnitude.
TV white spaces technology can provide long-range connectivity for farms, overcoming the cost barrier.
AI and machine learning can predict soil conditions across a farm using aerial imagery and sparse sensor data.
Tethered helium balloons are a low-cost alternative to drones for capturing continuous aerial imagery.
Farm Beats combines TV white spaces, sensors, and drones to create actionable insights for farmers.
Farm Beats technology can run offline, providing resilience against internet connectivity issues.
Farm Beats has been successfully deployed on farms, providing real-time insights on soil conditions and livestock.
The system can flag areas of a farm that need attention, such as water puddles or areas of differing soil pH.
Farm Beats' predictions are highly accurate and actionable, closely matching actual sensor measurements.
Farm Beats can monitor livestock health and movement, providing immediate feedback to farmers.
The project aims to bring these technologies to smallholder farmers worldwide, enhancing global food security.
Farm Beats demonstrates how non-agricultural technologies can be applied to solve pressing agricultural challenges.
The project combines advances in computer science with practical agricultural needs to create innovative solutions.
Transcripts
you
good morning everyone happier today
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
Revolution problem all over again how do
we get to this significant increase in
food production the agricultural
scientists have been think 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 they apply water uniformly
throughout the farm they'd 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 so
in the rest of this talk I will 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
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 and 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 other wife what
is my return of investment if I buy
these sensors which is so expensive that
is the goal of the farm beets 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 eight thousand to 80 and I'll
talk about a few techniques that we
think we can get that I think we can
help us get us get there before I go
that 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
her 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
that those were the worst four months of
my life every year but the reason but
what the reason was that there was no
electricity no toilets it was like in
going from a city to spend four months
in a village wasn't exciting but in a
way that kind of exposed me to the
problems of Agriculture and that's one
of the goals of the farm beets 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 farm beets project is to
bring down the costs 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 farmers house
in this case has some sort of
connectivity to the Internet they pay
for broadband they
one two 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 by 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 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 you know when
you're a 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 and noisy TV channels in a way
that did not interfere with the
reception in an adjacent channel so you
could be watching channel 7 at home on
channel 8 we could be sending Wi-Fi
signals and 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 father-in VHF they
go 12 times father 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
of a meter under soil and a signal just
keeps going through so this was a
technology we had built back into 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
the 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 there are
fewer people so if you go to a farm and
turn on an over-the-air 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
scientists 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
deployments we 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'd 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 sense that every 10 meters
is expensive to deploy to manage it'll
come in the way of the farmer as the
farmer does to 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
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
aerial 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
like maps such as the one I showed in
the previous
one would need one would put a few
sensors and then use either linear
interpolation or Cragen's method is a
state of the art but just use that to do
the prediction with this technology you
can use the Adel image the key insight
is that if two parts of the farm looks
similar either in RGB hyperspectral
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 in other for
other services as well one of the key
challenges in aerial 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
thousand dollars 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 aerial
imagery if we wanted to you 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 aerial 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 250 and 200 feet and they are
able to take continuous Edel 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
a 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 either 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 had actually
touched by the flood and only throws
away those crops in places like India
and Africa someone could just walk
around with the balloon or put it on a
bike or a tractor and
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'll move around
with Reindeer 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 of them 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
agriculture AG tech space a lot of them
are working on either sensors or drones
we believe you are the first who's been
able to combine them in a meaningful way
but we're just starting on the space
there's a lot more to be done but this
seems like a very promising approach to
build these maps at low costs with the
TV white 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
farmers house over the TV white spaces
apply machine learning but the
connectivity from the farmers house to
the cloud is not that great many farmers
they pay for broadband but all they get
is one to three 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 our
key insight was that most farmers have
pcs if they don't have a PC we ship them
a box
when everything inside this blue box
runs on the farmers PC takes data from
sensors from drones it's able to over
the TV white spaces it then does a lot
of this aerial image regeneration 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 that the
blocks and blue are the ones we've
already built the other ones are the
ones we were working on of course as I
mentioned my background is not in
agriculture so we partner with people
who understand agriculture the
agronomist farmers to build all those
services the other thing is that a lot
of data stays in the farmers house we
are not sending all the data to the
cloud for example the detailed drawn
imagery the gigabytes of video you can't
ship all of that to the cloud but we
transport it compress it and send a
compressed version to the cloud so these
are some of the unique features on the
left of this entire system it can also
run offline we can disconnect the system
from the year and their Ethernet and it
continues to run so just to give you an
idea of what or 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 it close to Microsoft campus
the small older farmer and the other is
a 2,000 acre farm in upstate New York
the kind of insights we can provide to
the farmers or I'll walk you through
some of them this is a full 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
would fly the drone take the SD card out
go to a city upload all the data wait
for 24 hours by that time the strake I
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
said 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 so 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 ten of these how
accurately can we predict their 990 are
the values using the farm beats
technique of using aerial imagery with
drone sensor data so these are the
results for temperature pH and moisture
this is how accurate farm beats is and
this is how accurate the actual sensors
were that is the sensors for example we
use 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
are 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 barns streaming data with the
TV white spaces very able to flag cows
or the cows are moving around well
whether 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 all the analysis in the farm
itself so you can that's what farm beats
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 Shawn mark and Kristen
just amazing people so with that I
wanted to end the talk thank you
[Applause]
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