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This is MIT Technology

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Review.

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From the cornfields of Iowa

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to outer space, scientists are

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building a world where plants and machines

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communicate their needs with us and

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each other in real time.

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Are you ready? I'm

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ready. Okay.

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We're inside an experimental tractor on a test

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farm that belongs to John Deere. So

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we're sitting in a 8RX tractor, 410 horsepower.

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This machine we're in is loaded with

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tech. It has self-driving capability.

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Joe Leifer is the senior product

0:45

manager of Autonomy. This machine

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also has the capability to run full infield

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autonomy. Just through the click of a

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button here on this lever, right, I can change

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ultimately the ground speed that we're running at.

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It can run in straight rows or curved

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ones, stay within the boundaries of a field,

1:01

and knows where it should place seats. So

1:03

you've got a fair number of sensors,

1:05

cameras. Absolutely, yeah. It's

1:07

equipped with six stereo camera pairs, three

1:10

on the front of the tractor, three around the back of the

1:12

cab. We have two NVIDIA

1:14

Jetson GPUs that are classifying

1:17

the world around us as we run in full autonomy

1:19

mode. So we just came to a stop,

1:21

and I'm going to power down the tractor here now.

1:29

In some ways, AI

1:30

has arrived at scale on the farm. A

1:33

majority of farmers now use sensors and

1:35

digital technologies that collect data,

1:37

and most tools that analyze that data also

1:40

use AI. But it's still early

1:42

days for other forms, including machine

1:45

vision.

1:46

I imagine a world within the next 10

1:48

years where all of these vehicles

1:51

literally have eyes and maybe

1:53

ears. Those eyes and ears,

1:55

those cameras, those sensors

1:58

are powered by machine learning.

3:57

years.

4:01

It's looking for is the machine

4:03

being a little bit too rough on the inside and

4:05

damaging the kernels. And if it is,

4:07

it begins to make adjustments to its mechanical

4:09

systems. And that's what you'll see back

4:11

here. All these mechanisms are meant to just

4:14

distribute all of that residue

4:16

in an even fashion because another

4:18

job of the combine is to get the ground ready

4:21

for next year. So by evenly distributing

4:23

all the residue, that becomes essentially

4:26

fertilizer for next year's crop.

4:28

Not all of this is done without the vehicle

4:31

ever stopping. And then this big tube,

4:33

this big auger above us, folds

4:35

out basically 90 degrees

4:38

perpendicular to the vehicle. And

4:40

then a tractor pulls up next to it with

4:42

another wagon. And then the grain

4:44

that is stored here is transported from

4:47

the machine through that auger to another vehicle

4:49

pulling a wagon. Looks like

4:51

cameras or sensors up on the arm as

4:53

well. Oh yes.

4:54

One of the pain points of farmers in the past

4:56

was from the cab of this vehicle,

4:59

from the operator station of this vehicle, it's

5:01

sometimes hard to see because the

5:03

tractor may be far away and it's dusty. So

5:05

we've built a machine vision system that helps

5:08

aim

5:09

where that auger dumps the corn.

5:12

So shall we hop on? Please. Let's

5:14

do it. All right. But it's more

5:17

of a climb than a hop. This machine towers over the both

5:19

of us and the latter feels at least

5:21

a few stories tall. And

5:24

why don't I let you jump in the driver's seat?

5:26

Ooh. Thank you.

5:29

We are sitting now in the cab,

5:31

the operator station. This vehicle

5:34

has 17,000 parts and all of those 17,000

5:36

parts have to work in synchrony

5:38

to essentially

5:42

create what we refer to as a factory on wheels.

5:44

Up inside the combine, I'm able to get a

5:47

better sense of how it works. He

5:49

describes how AI talks to mechanical

5:51

systems in real time to make

5:53

sure the harvest, in this case corn kernels,

5:56

isn't damaged. It's also recording

5:58

the crops quality and yield.

6:00

and it provides farmers with a report card,

6:02

so to speak, called a yield map that

6:05

allows them to assess their performance.

6:07

In the 90s, we talked about one meter

6:09

accuracy. In the 2000s, we would talk

6:12

a few inches. We

6:14

are now at a point where the accuracy of

6:17

our GPS solutions is

6:19

less than one inch, 99% of the time.

6:22

Which matters when you're placing

6:24

something as small as a seed. It matters a

6:26

ton. And actually, if you put

6:29

two seeds

6:30

next to each other, but you

6:32

put them too close to each other, they

6:35

begin to compete for nutrients right away.

6:38

If you space them apart just enough,

6:40

then each one kind of gets their own little ground

6:42

and they grow the most healthy.

6:47

A

6:47

common misperception of farmers is that

6:49

they reject new technology or their

6:52

late adopters. But he says that's far

6:54

from true. After all, farmers

6:56

accepted self-driving technology much

6:58

earlier than the rest of us. But they

7:00

do have what he calls a show-me culture.

7:03

They need to know these tools work and will

7:05

work for them on their land and

7:07

for their crops.

7:10

Let's say there's a farmer that farms

7:13

a thousand acres just to make it a round number. What

7:16

you'll see farmers do is test

7:19

out technologies in small portions

7:21

of their farms. So they'll say, hey, I'm gonna take this small

7:24

field that I have and try this

7:26

out for this year and see if it scales. They

7:28

don't just do that with equipment. They do that also

7:30

with seeds and with chemicals and

7:33

even new agronomic practices. So

7:35

they wanna understand how that

7:37

a new practice or a new technology would scale

7:39

across their operation. He

7:42

believes AI on the farm through

7:44

the lens of data analytics has arrived

7:46

at scale. But

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we're still just getting started with machine

7:50

vision and other forms of AI.

7:53

Right now, the machine

7:56

reactively makes decisions based

7:59

on what it injects.

7:59

and it optimizes itself based

8:02

on what's inside of it. As we look toward the

8:04

future, with more machine vision capabilities,

8:07

the plan is to begin to look ahead of the machine

8:09

with cameras and be able to predict

8:11

what's ahead of it. So you can actually,

8:14

for example, measure the height of the

8:16

crop ahead of the machine, and the machine

8:18

can almost begin to configure itself

8:21

proactively.

8:22

Another way to do this is through satellites.

8:25

That information

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can be then transferred to the machine via

8:30

the operations center, and now the machine

8:32

has knowledge of what that satellite

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saw, and again, can begin to proactively

8:37

configure itself before it actually

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gets to the crop. We believe that

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persistent and reliable connectivity

8:44

is critical to agriculture. It's not just moving

8:46

data off the vehicle, it allows them to

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have access to data that could be moved

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from other places into the vehicle so that they

8:53

become more intelligent in real time.

8:54

Right, so that if you have multiple pieces of equipment,

8:57

they can essentially talk to each other. Exactly, exactly.

9:03

After the break, plants that have been genetically

9:06

modified to communicate their needs by

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giving off new forms of light that can be

9:10

seen from outer space. You

9:15

can find links to our reporting in the show notes,

9:17

and you can support our journalism by going to

9:19

techreview.com slash subscribe.

9:23

We'll be back right after this. We're

9:30

mining soils in a rate

9:31

that's never been seen before. Those soils took thousands of years to develop, and

9:34

they might not be usable in 30 years,

9:37

and

9:37

people do not understand that. So we gotta do something, and

9:40

it's likely technology and the win-win that's

9:42

gonna save it, right? You have to make farmers more productive,

9:45

more profitable, more sustainable. You

9:48

can't take anything from the soil and

9:50

just throw it away. So we have to do something. We

9:52

have to do something about it. We have to do something

9:54

about it. We

9:55

have to do something about it. We have to do something about

9:57

it. develop

10:00

essentially complicated solutions that give them an

10:02

easy work around. I'm

10:05

Shelly Aranov, I'm the CEO and founder

10:07

of InnerPlant. We

10:10

create crops that can communicate what they need. So

10:12

let's start at the beginning, actually. We use 250

10:15

billion worth of chemicals every year, fertilizers,

10:18

pesticides. At least 30 percent of those

10:20

are misapplied, overapplied. At

10:22

the same time, we lose 20 percent of potential yields

10:25

to pathogens. So we have an inefficient system.

10:27

And the reason it happens is because farming is really

10:29

large. Right. An average farm is probably

10:31

the size of San Francisco. And

10:34

it's impossible to find problems at the right time. So

10:36

what farmers do is they hedge their bets, they

10:38

apply chemicals in advance on entire fields, and

10:41

the data clearly shows that that doesn't give you better

10:43

results. It just gives you more chemicals. So

10:46

what we do is we enable crops

10:48

to tell the farmers what they need.

10:50

This is achieved by genetically engineering

10:53

plants to give clear signals about what's

10:55

wrong with them. So it starts with the plant

10:57

itself. Plants already

10:59

react natively to stress. So

11:02

people think that plants are just stuck

11:04

and do nothing. They're super active in their environment

11:07

because they're stuck and they can't move. So,

11:09

for example, if a plant is eaten by insects,

11:12

it will actually start producing a new compound in

11:14

its leaves to make it taste bad. Or if

11:16

it doesn't have enough nitrogen, it's going to start mobilizing

11:18

its roots to be able to capture more nitrogen

11:21

from the soil. All these things happen

11:23

very early on and they're different reactions.

11:26

And we know them very well

11:27

because people have been studying them for a long time. But

11:29

they're all on the biological level, so you can't see

11:32

them. What we do is we code the plants

11:34

as they're reacting to that native stress. They're going

11:36

to start producing a new protein that signals

11:39

optically from the leaves.

11:40

There is a problem. I'm attacked by insects. I

11:42

have fungal pressure. I need more water. And

11:45

then we use sunlight, optical

11:48

equipment and algorithms to be able to

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see those signals from anywhere from satellite imagery

11:52

and as close as, you know, a tractor sensor

11:54

in the field.

11:55

And this is how it works. What

11:57

we do is we go in. take

12:00

that piece of DNA and

12:02

we add something to it. We add the fluorescent

12:04

protein. And then what happens is that

12:07

when the plant starts activating that piece of DNA,

12:09

it's going to start producing the new fluorescent protein

12:11

that we taught them how to make. So that's really it. And

12:14

then it's embedded in the plants. So in

12:16

the next generation, they already have this line

12:18

of code. And they just know that when

12:21

they're responding to stress, they're going to start producing this

12:23

new protein. And

12:25

also, when the stress goes away, the protein

12:27

stops,

12:28

which means you have a signal that's on and off and

12:30

you know when problems are resolved.

12:32

What she's talking about is called solar-induced

12:35

fluorescence. It was discovered about

12:37

two centuries ago by a Scottish preacher

12:40

who figured out that when sunlight hits a green

12:42

alcoholic extract of laurel leaves, it

12:45

brings out a bright red light.

12:47

It's part of the photosynthesis process, but

12:50

not something the human eye can usually

12:52

see without help.

12:53

And the reason that's important is because if you just need

12:55

the sunlight, then you can do it from satellite

12:58

imagery. If you need another light source,

13:00

then you're going to have to be closer to the ground

13:02

and it's not scalable. And if you want to cover

13:04

oceans and the entire globe and large agriculture

13:08

land, you need that.

13:09

Before she got into the agricultural business,

13:12

she was in the food business.

13:13

An Israeli-style hummus brand in the Bay Area. And

13:16

at the peak of that, we're selling at about 300 stores. And

13:19

then I had my first daughter, and I thought,

13:21

I want to do more with life. I want to

13:24

make an impact. I want to do something that matters. I

13:26

really want to work with agriculture. I love food,

13:28

but I wanted to go back to where things start and

13:31

where a lot of impact could be done.

13:34

Do you want to see the demo? What you'll see,

13:36

if you put on the glasses.

13:40

And I'm putting on the

13:42

headphones. Feels like sunglasses

13:44

I'm putting on here. Oh, you kind of are. So

13:47

basically, what you'll see is we have two kind

13:49

of plants here. These are the regular plants,

13:51

and these are the sensor plants. Oh, wow. Exactly.

13:54

So they really glow. And here, if

13:56

you think about it, the laser and

13:58

the color.

15:38

This

16:00

one has the problem and I can treat just

16:02

in the places where I have the problem

16:05

and skip the guys that

16:07

do not.

16:08

First, he needs to understand the

16:11

way a plant responds to problems. One

16:13

way to do this is by grinding up infected

16:15

plants and comparing their gene expression

16:17

levels to healthy plants,

16:19

as the stressed plants will have turned on

16:21

a bunch of genes that help combat the

16:23

problem.

16:24

And once they know what those genes are,

16:27

they can add their color signal onto that process.

16:30

Yeah, go ahead, go in here. There's

16:33

no pathogens or anything in this one. At least we try

16:35

to keep the pathogens down. There are no

16:37

intentional pathogens in this growth chamber.

16:40

So we have tomatoes, rice

16:42

and soybeans in here right now. So

16:45

we're developing sensors for all of

16:47

these. If they come in here, we grow them in

16:49

here, we do the tests in the lab, and

16:51

then the best performing ones go out into

16:53

the field.

16:57

So that's sort of dirty. So that's

16:59

where we grow the plants in the soil. This is

17:01

where we grow the plants in tissue culture. So

17:03

this is more, well, called sterile,

17:06

but it's a cleaner environment. So here's

17:09

where they do the actual integration

17:12

of the genes into the plant. And

17:16

so we need to keep these guys sterile because they grow in

17:18

these tissue culture environments until

17:21

they're big enough to be moved over

17:23

into soil.

17:29

They are generating the plants. They

17:31

start from like little tiny clusters of cells

17:33

and then through different manipulations

17:36

of media. So I have this little cup here

17:38

with plants that they

17:40

grow up. Some of the plants that are

17:43

non-edited or non-changed

17:45

or transgenic, they turn white. They

17:48

don't live, but the ones that are starting to turn green, those are the

17:50

ones that will make it through. So they're in these little

17:52

sterile cups. So all this stuff in here is sterile. We

17:54

don't like to have to get the soil in here, not

17:57

until we can move across the way. And then

17:59

we take

17:59

them across the hall. and then we put them in dirt and then

18:01

eventually take them out to the greenhouse where we can collect seeds

18:04

and then in the next generation we can start

18:06

doing our

18:06

tests.

18:11

We do two detections currently,

18:14

so two different colors, and then we can do up to five.

18:17

Once again, Shelly Aronoff.

18:19

And the idea is to get to the point where we can tell farmers

18:21

everything they need in one seed. So

18:24

fungal, insect pressure, nitrogen,

18:26

phosphorus, potassium. And

18:29

the plan is to do this at scale. The

18:31

technology is embedded in the seeds, right? When the farmers

18:34

plant seeds, they're harvesting data and

18:36

they don't need to do anything else. It's just a line of code

18:38

in the seeds. So every single plant in

18:40

the field is emitting this information. It's

18:42

a living sensor. And then the idea

18:44

is first, salad imagery, because we want to make

18:47

sure that we can cover the globe really cost effectively.

18:49

And we're talking two and a half acre pixel

18:51

size. So we can look really, really small and

18:54

start seeing an infestation in the early days, for

18:56

example, if it's fungi.

18:58

So this is the starting point. We call that the scouting

19:00

tool.

19:00

But then once you get into the field, right, we're talking about

19:03

the OEMs and the tractors, then

19:05

you have another opportunity because you already know

19:07

you have a problem, but you have equipment

19:09

now that can literally look at every individual plant

19:12

and give it just what it needs.

19:15

Right? So in the really a few years into

19:17

the future, we can go into the field

19:19

and scan every plant knowing, for example,

19:21

there's a nitrogen stress somewhere,

19:24

but then still look at every individual plant and only

19:26

give the plants that need nitrogen, nitrogen

19:28

and the plants that are healthy for whatever reasons, those

19:31

can just be left alone. And

19:33

that will be, I believe, the most efficient

19:36

way we'll ever farm.

19:43

This mini series on satellites and farming

19:45

was reported and produced by me, Anthony

19:48

Green and Emma Silikens. It was edited

19:50

by Matt Hounen and our mix engineer is

19:52

Garrett Lang with original music by

19:55

Garrett Lang and Jacob Gorski. Thanks

19:58

for listening. I'm Jennifer Strong. This

20:05

is MIT Technology Review.