I'm a computer scientist,
and I'm a professor
in computer science at UC Santa Barbara.
The SmartFarm project, which
I want to tell you a little bit about today,
is a project that
Professor Rich Wolski and I started back in 2014.
It actually started with Olivier.
He participated in a capstone class project effort
that I was putting together that brought in
industry partners to work with students.
He brought a lot of the ideas that moved us
from the Cloud computing area to applying,
bringing technology to agriculture,
which was extremely under-served and
still is today in my opinion.
I appreciate that, and I have been working
closely with Olivier throughout since.
This is an effort that's funded by
NSF funder sustainability call, that's called CyberSEES.
This was a joint work with agriculture specialists
and researchers that Calpoly SLO, and Fresno State.
The ideas that motivated us,
or that this project is really targeting,
is to try to identify technologies
in other parts of
industry and society that might be useful,
or might provide the building blocks,
that can be used to solve
some major problems in
the AG to make automation possible,
to do actuation and control of on-farm processes.
Our approach is to focus
on Cloud computing and data analytics.
Similar background as Olivier talked about,
that AgMonitor pursues, but also
to consider energy use in programming systems,
and try to see which of
those technologies make sense to apply to
systems that are on-farm
and that actuating control
on-farm processes and operations.
We focus on how do we bridge the gap
between technologies like Google Maps,
and Amazon.com, and Netflix,
and bring those recommendation systems
to fruition to provide growers and ranchers,
especially smallholders,
with tools that they can use to solve
problems and to scale
without using more land and resources.
We look at how do we drive down the cost of sensing?
How do we make these systems super easy to use?
Because farmers are focused on farming,
they don't have a lot of extra time
to learn new technologies,
so they had to be super easy to integrate and use.
How do we preserve privacy?
Then how do we integrate with a lot of
the technologies that are already in use on farms?
In particular, sensors and data that they collect.
Our key innovation is to do
something different, to be disruptive.
Most companies, or a lot of companies today,
will provide sensors on
farm for farmers to gather information,
and then they use typically
cellular connections to broadcast,
to bring up that data to a Cloud system.
As farmers get more and more sensors,
this becomes pretty costly.
Also pushing all that data to the Cloud takes time,
and energy, and money,
and bandwidth that make a considerable,
perhaps we can do this differently.
Agriculture is different than Netflix,
and Amazon.com, and electronic commerce.
It is vast areas that we have to be able to extract
information about so that we can
provide the farmers with the tools that they need.
There's already lots of sensors out there as I mentioned,
and some farms aren't
fully connected in these large areas,
and some don't have Internet connectivity at all.
We have to be able to reach these growers
and ranchers that may not
have the state of the art equipment,
but we have to be able to help
them with technology if we can.
Another key issue is that when you have
a bunch of sensors on farm or on your farm implements,
and you move that all to the Cloud,
your data becomes shared
with the vendor that you're sharing it with,
the vendor who has provided you with that sensor.
This data is an economic advantage,
and so the sharing that
farmers and ranchers need to have control,
or at least some say in who they share their data with,
and how that data is shared beyond the farm.
The other thing that's unique about agriculture,
rather than all the other things
that we use the Cloud for,
is that decisions are local.
How much water a farmer
here in Santa Barbara, the strawberry farmer,
is putting on his Strawberries,
has really no impact to
a Cranberry grower in the Northeast.
What we want to do is be able to give
the farmers tools to make local decisions,
and then share data only as
far as necessary to solve problems.
If there's, say a pest or
disease that's moving across a county,
maybe you want to share data at the county level,
but you don't necessarily need to share it across states.
You want to have a real intelligent system
for data sharing,
and that's part of our approach.
Our approach takes the Cloud
and all the building blocks from
the recommendation systems that are
popular for Internet computing and e-commerce,
and shrinks them down into
a usable system that can actually run on farm.
Instead of moving all that data to the Cloud,
what we do instead is we take the Cloud, which is code,
and that's a lot smaller than
the data we're collecting with all these sensors,
and we move the code which is tiny, to the farm.
In order to do this though,
we have to run computer systems on
farm so that farmers can do
all of the data analytics or the fusion,
bringing all their sensors together,
get intelligent, actionable insights from their data,
that all has to be local.
We study at UCSB how to do this efficiently.
These computer systems have to be an appliance.
You've heard of self-driving cars.
This is a self-driving computer.
If we can do cars, we can do computers.
This computer is self-managing,
it runs for years without any operators,
you don't need an IT staff.
This is really important for agriculture,
but it also feels,
and the way you use it,
is the same as you use an app on your phone,
or you interact with Google Maps,
it has to be that simple.
We look at how do we build
systems like that to make it look and feel just
like you're using an application
that's online that you're comfortable with.
We also then consider
the machine learning and artificial intelligence
that goes into the recommendation systems that
are happening in e-commerce to see
which ones are going to make sense to provide
recommendations for agriculture operations.
We put all of this into
this small computer system called an Edge Cloud,
which runs on farm that
the sensors interact with locally.
What's nice about this is the data goes only as far
as the farmer says that they want that data to go.
Instead of just automatically moving the data,
this farmer is in control of that sharing.
They can share if they want, they don't have to share.
They can share anonymously.
This gives us a lot more control,
or gives a farmers a lot more control,
over the decisions and the operations that they have and
that's important for their economic advantage.
I wanted to share with you a couple
of the end solutions
that we target with the smart farm system.
One is really similar,
where fits right in,
and it's similar to what Olivia was saying.
It's an analytic system for zone identification.
When you have a field, there is a lot of variation.
Bruce Bahrami asked a question in the Q&A earlier.
There's a lot of variance
in the data that we're collecting on the farm in general.
These are vast physical systems, complex systems.
How do we manage this?
Olivia showed us how you could use
images taken from satellite data or from
drones to identify areas
on the farm that have similar characteristics.
We did something similar with smart farm in
that there is a really inexpensive way to gather
soil properties or data that is correlated
with soil properties called electrical conductivity.
This is a device that you pull behind
a tractor so you can continuously,
you can repeatedly take measurements,
and those measurements are correlated
with soil properties and soil health.
What this does is it gives you a really inexpensive,
fast way to continuously,
over the course of a season or
every year, multiple times,
collect data that then our system
processes and identifies regions of similarity.
Now, Stewart Wolf said earlier that you don't want
to do precision application at a tree level.
But we are no longer just applying
one vast application of an input across an entire field.
We can do site-specific management that finds
the middle ground of this decision-making.
You can find areas that can be treated
similarly so that you only apply the inputs,
the water, the pesticide, the fertilizer,
in the way that soil or that area requires.
Then you can have very small areas or very large areas,
depending on how efficient it is to apply those inputs.
Another system or end solution that we have
been pursuing has to do with frost protection.
When there's going to be frost,
say for the citrus industry or the strawberry industry,
there's lots of ways of trying to protect against frost,
if it's a hard freeze,
which can destroy a crop.
It can do lots of damage and it's extremely costly.
One main way of trying to
prevent or protect against frost is to move
the air during the hard freeze to keep
some warmth in the air so that
the plants or the fruit don't freeze,
and they use very large fans for this to move the air.
You can also use irrigation,
but both of these solutions are extremely costly.
I've spoken with some farmers where
the application of water for a one-night frost event,
for a significant frost event,
costs and uses as much water
as an entire season of growth.
From the fan's perspective,
these are huge polluters.
They have huge engines that are driving
these fans and so it costs money for gas,
it puts a lot of pollutants into the air.
You really don't want to use them until
you really have to.
But it's really hard to predict when frost is going
to occur and to what extreme,
what duration it's going to hold.
You need to understand
the typography and
the physical characteristics of the farm.
We've worked on putting
together a sensor system that allows
us to really identify fine-grain micro-climates.
We call them nano-climates because they're really small.
The water features on a farm, or the hills,
or the plants themselves play
a role in these tiny little microclimates.
What we're able to do is use a combination
of a small number of high resolution,
very expensive sensors, very small number with lots
and lots of really low cost, low-resolution sensors.
Then use software that bridges the
two so that we can very accurately
identify nano-climates at a very low cost
with the sensor system because of software.
The smart farm allows us
to really bring down the cost of identifying
these nano-climates so that we can more
accurately dial in when to put on water,
what duration, and where on the farm to put on water,
or to have to actuate and control these fans?
Maybe even in the future, we're thinking,
working with fan companies which we've started
to make smaller versions of these
so that they are more energy-efficient and we
can still prevent frost in an economical way.
The last one I'm really excited about something
that we're focused on now.
This is ongoing work that we're actively engaged in,
and this is a more far-out,
far-reaching vision where we believe that in the future,
for various reasons, that we will be working
with controlled agriculture environments.
This is where there is some form of
structure that is used to
protect plants from various things,
from the environment to the wind and the sun,
from cross-pollination, from pests.
The one that we focused on,
you might have heard of urban farming
or rooftop farming, vertical farming.
These are very next-generation ideas that allow us to
experiment with agriculture processes
and automation may look like,
but we look at something that's more near-term.
We look at citrus under protective screens.
These are screening systems that are semi-permeable,
we call them cups for short.
Our team has access to the first production size,
four acres, experimental CUPS in California,
at Lindcove Research and Extension Center,
and we are studying CUPS with new growth,
new trees for citrus to try to
exclude this psyllid, this pest,
ACP, that brings this citrus greening disease,
which is a bacteria that's called HLB.
This is a deadly disease.
It's devastated the citrus industry.
It's coming to Central Valley of California
and lots of people are studying it.
One way to protect the trees against ACP,
the insect vector of HLB to produce
HLB-free citrus are these screening systems,
which the mesh is small
enough that it keeps out the insect.
If you have this, which is
necessarily an added cost to the farm,
we asked the question in our research,
what are the ways we can leverage
the structures to do
things that are much more energy-efficient,
that are much more automated,
because we have the structure to work on?
We have heavily instrumented CUPS system at Lindcove.
We are studying how we
combine sensing with physical modeling,
with machine learning to predict where
the different microclimates or
the nano-climates are going to be under CUPS.
How they're going to be different than the outside.
Therefore, what operations need to be changed in order to
produce very productive, very high-quality fruit.
We are also studying,
if you have a structure like this,
these models are going to help
us understand how pesticides
behave when they're sprayed under these structures.
How they move through the air,
how they move through the trees so that we can
help specialize and automate pesticide application,
so it's extremely precise to
reduce the cost of applying pesticides.
But also then do it very effectively,
so we eliminate the pest that are
allowed into these CUPS systems.
We also are working with
Steve Dunbars to understand how we might capture
insects that are still under
CUPS or are outside of CUPS trying to get
in using interesting lighting and smart traps.
We also have research
that looks at how do we detect breaches.
As soon as there's a whole,
we have to find ways of making
sure we exclude the pest that are
being kept out and breaches can happen all the time.
You drive a tractor into the screen.
These things are going to happen in real life and
we have to automate the process of detecting,
alerting, and fixing, responding to such actions.
I think one other thing that's
exciting is when you have a physical structure and
it's actually moving and allowing some wind through,
that movement can be leveraged to harvest
electricity for the automation
and the robotics that are happening inside.
We are looking at all kinds
of the different platforms for
the next generation of
controlled agriculture and environments here at UCSB.
All of our technologies and research are real,
we put them in testbeds.
One thing that Olivia taught me is that you
have to show that it works in real life
and demonstrate it to farmers if they're going to
use anything that we developed here at UCSB.
We have taken that to heart and all of
our students work out in the field, literally.
This is their cubicle.
Lastly, I just want to emphasize that we're doing things
differently here at IEE and at UCSB,
computer science and computer engineering research
here is unique because
it is problem-driven and empirical.
It is multidisciplinary.
We work with the community and with farmers and
growers in order to enable our advances.
Of course, I can do any of this without
an amazing team of supporters,
and researchers, and students.
I thank them all, and especially
the Institute for Energy Efficiency,
and I thank you-all for listening.