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0:13

Hello, everybody. It's Tuesday the third of

0:15

January twenty twenty three. Happy

0:17

New Year. think we can say that with

0:19

Garik Mitchell here and Bill Thompson to bring you

0:21

digital planet for this week from the

0:23

BBC. Are you alright, Bill?

0:25

I

0:26

guess. You're indeed there. Yes.

0:28

Good. Yeah. To be back. The year has begun.

0:31

Yeah. I

0:33

hope it will be friendly.

0:36

Yes. Let us hope. Well, you mean between

0:38

you and I, it's always friendly. I was

0:40

thinking more broadly, actually. was thinking

0:43

Given our global audience, I hope it's friendly

0:45

for

0:45

everyone. Life beyond the little parochial

0:48

hearings on between your NII, Yes.

0:50

Of course. And well, just to get

0:53

us in that New Year spirit, I don't know

0:55

if we can posit it as a New Year's tech

0:57

resolution, but whether people

0:59

should be backing up their

1:01

Twitter profiles. We've spoke about it

1:04

at the end of last

1:05

year, didn't we towards the end of last year?

1:07

And

1:08

We did. Yep. It's triggered a

1:10

little bit of response. So

1:12

It may be a new solution for some people. I

1:14

don't know. But inspired

1:17

by your piece, Bill, where you did

1:19

say a bit about how we can archive

1:21

profile and

1:22

momentum. I just talked about the fact

1:24

that it's clear that like any

1:26

other online service Twitter is a little

1:28

bit fragile. And if you value what you've been

1:30

saying there, it's and a good idea now

1:33

to to just take copy of it. It should

1:35

be good good practice anyway, good digital

1:37

hygiene to keep your own copy of anything

1:39

you post anywhere that you see because of interest,

1:42

because you can't rely on any third

1:45

party provider to keep it, or to care

1:47

about it the way you care about

1:48

it, that's really that way. I get it. Well,

1:51

Francis Day says one reason

1:53

to do it might be you could marvel at why you

1:55

got caught up in things which now seem irrelevant.

1:58

But there is oh, yes. I mean, I think I think

2:00

if you did it if you crawled through my

2:03

Twitter archive, you climbed a whole load

2:04

stuff. Did I really waste

2:06

my time thinking about this? Yes.

2:09

Yes. Thinking about

2:11

it, then tweeting about it. And then thinking

2:13

about the things that people replied yet, we've

2:15

all done it. Peter Smurten

2:17

kind of related to this really says, well, my

2:19

thinking is that you ever place anything on

2:21

Twitter that is worth preserving, then you're

2:23

doing it all wrong. He's added a smiley face

2:25

there. Jessica, it's rid of

2:27

I

2:27

believe. But but but that's actually a

2:30

very interesting point because it

2:33

it definitely was a sense that Turtle was a

2:35

place where you would put stuff that was ephemeral

2:37

as it works intended just to go away. But

2:40

then when threads were created and

2:42

used, people started using it to put up

2:44

really quite interesting collections

2:47

of thoughts. That possibly worth

2:49

preserving. And so people found a

2:51

way to use this this medium that

2:53

that was sort of unstructured. In

2:55

a more structured way. And as a result, over

2:57

the years, I think there are

2:59

repositories of wisdom embedded

3:02

in people's treatifeds that may be worth preserving.

3:05

But absolutely right. That was not the original intention.

3:07

Mhmm. It was definitely supposed to just go away,

3:09

you know. Well, thinking about my early tweets from

3:11

Lindsay about here where I was drinking cups of coffee.

3:14

You know, what sort of a nice cake I had and

3:16

things like that. There were not exactly significant

3:18

insights into my life.

3:21

Alright. Folks, by the way, we have an agricultural

3:23

tech special coming up in just a few moments when we

3:25

get into the radio program. In case people were

3:27

impatient to get into that which we have two

3:29

more listener suggestions, then we'll get into electric

3:32

tractors and smart traps

3:34

and other stuff like that, Bill. You

3:36

go for it. Oh, let's go for it with Sonya

3:38

Livingston. He says, I think there's

3:40

a difference between conversations and

3:43

sharing information that has value at the time

3:45

and wanting to keep the records for

3:47

posterity. Yeah. Bit

3:49

of a

3:49

difference. Yeah. Yeah.

3:51

Now what was a good insight from Sonya? Yeah.

3:53

Thanks, And Caroline Talbot, just

3:55

to finish this little section for us, says,

3:57

a lot of innovative commerce, serious

4:00

professionals used Twitter to share

4:02

and get feedback on their work with fellow

4:04

professionals and the public, for instance, scientists

4:06

and medics studying COVID, probably

4:08

important for them in many ways to retain

4:11

their archives. And, yep, I

4:13

know a lot of scientists and they will say

4:15

that they find I think out of all the networks,

4:17

they I don't know if it's changed now, but they

4:19

will say that Twitter is the one for them in

4:21

terms of sort of peer

4:23

discussion of things going on in

4:25

science in their field

4:26

outside. Maybe the the more formal channels I

4:28

should have. Anyway, Yeah.

4:31

So my last point and I know we need to move into the

4:33

program now is the architecture that gives

4:35

you is a starting point when it's not enough

4:37

in itself. You need to use other tools to

4:39

get the lists of who your contacts are

4:41

and any links and stuff like that. So don't just

4:43

see you can download your archive from Twitter and then

4:45

you're

4:45

done. You do need to put a little more effort

4:47

into this. Oh, alright. I'm glad you

4:49

added that because I must admit I'm one of those people who

4:51

just wants to get it all done and I just downloaded

4:53

the archive and

4:54

said, right, done it brilliant. You can kill it off

4:56

now. I don't care. But it's not as simple that says

4:58

build on some on digital

4:59

panels. Absolutely not.

5:00

Okay. Absolutely not. Thank you, Bill.

5:02

Alright. Well, let's jump into the rodeo

5:04

program. This is some cultural

5:07

technology coming your way as it's handed

5:09

on the radio this week. Hello,

5:11

everybody. Happy New Year. I'm Gareth Mitchell,

5:13

and this is digital planet. Today,

5:15

we're talking agricultural technology.

5:17

Yep. It's a special edition with

5:19

an electric tractor, a

5:21

smart insect trap, and a

5:23

robot fruit picker. And I'll be harvesting

5:26

comment and analysis from Bill Thompson

5:28

today as it goes. Hello, Bill? Hello

5:30

there, Gareth. Nice to be here again.

5:32

Likewise, to be speaking to you.

5:34

Okay. Well, let's start with that electric

5:36

tractor. It's all part of the drive,

5:38

of course, to low emission agriculture.

5:41

And out in the field, the machine isn't

5:43

just harvesting crops. It's harvesting

5:46

data, thanks to an array of

5:48

onboard sensors to monitor everything

5:50

from plant health to pests.

5:52

The company, monarch tractors,

5:54

launched their autonomous electric smart

5:56

tractor just before Christmas, CEO

5:59

and cofounder Praveen Penmetze

6:01

says it's the first vehicle of its

6:03

kind to be commercially available.

6:06

Yeah. So our tractor is quite special in

6:08

the sense. It's not just an electric tractor

6:10

gear. It's also a driver optional.

6:13

And more importantly, it's also smart.

6:15

It tells the farmers what is going on in the

6:17

farm

6:17

today, so it gives them alerts. And

6:20

also the data that it collects means that

6:22

the farmers can use that to go back

6:24

in time and see what happened that

6:26

led to, you know, the the harvest

6:28

that they have had and use

6:30

that data. To

6:33

not only save money, but also make

6:35

more money by telling their story to the

6:37

customers. So the tractor does all those

6:39

three

6:39

things. So as an electric vehicle

6:41

I suppose my husband's question is we know that tractors

6:44

need to be out in the field

6:46

for long periods at a time.

6:48

And it's all very energy intensive

6:50

work. How long does it last for on

6:52

a single charge?

6:53

Yeah. So our tractor, even

6:56

if you're doing some light activities, we'll

6:58

ask for, like, fourteen hours and sometimes

7:00

even more. But if even if you're doing,

7:02

like, really heavy activities, Garrett,

7:04

like, say, tilling or other

7:07

land management activities. Even

7:09

then, we get the tractor to, like, five to

7:11

six hours of usage. So that

7:13

means that a power can, you know,

7:15

finish the shift. And while they're having

7:17

lunch or taking a

7:18

break, they get to recharge the

7:20

tractor or swap the battery out

7:23

in the middle of the field and then

7:25

get the tracker up and running just like you

7:27

do with your

7:27

power tool. No. I like that analogy.

7:29

The tractor is also driverless then. Yes.

7:32

And one of the things that we learned to have

7:34

this bigger infinity girth when we

7:36

first built our first electric back

7:38

in twenty seventeen. We took it to an Indian

7:40

village. And the farmer looked at

7:42

us and said, this is great.

7:44

Who's going to drive it? And I

7:46

was, like, surprised because I was, like, wait a

7:48

second. I thought you're going to drive it. And

7:50

the farmer said, no. There's only two people in the

7:52

whole village who can drive a tractor

7:54

because it's not just about driving the

7:56

tractor. It's about doing operations. Those

7:59

operations are now fully automated. It, which

8:01

means it's driver optional from that standpoint.

8:03

And that makes a big difference for followers

8:05

around the world. Right. And that's the key point.

8:07

Driver optional. So a driver can be in

8:09

the cab if they want to be,

8:11

but it has a driverless option. Absolutely.

8:15

And the reason for that is how many times have you

8:17

not seen a tractor on a road. Right?

8:20

Yeah. I do want to drive

8:22

around it. I seem to

8:24

be reassured. Yes. Now

8:27

you described this as a a smart tractor and

8:29

all the time on this break when we have people telling us,

8:31

oh, we got smart this stat and everything smart

8:33

these days. But let's unpack what that

8:36

means lots of senses on

8:37

there, lots of processing. Like, I

8:39

remember a conversation where I was talking to

8:41

this farmer about all this

8:43

fantastic data that they're gonna

8:44

get. And the farmer looked at me and said, if

8:47

you're gonna give me data, I'm gonna charge

8:49

you. And

8:50

he said, what I want to know

8:53

is what action needs to be taken

8:55

just giving data or telling

8:57

somebody what's happening with the

8:59

tractor or what's happening on the farm

9:01

is not

9:01

enough. We have to make it actionable

9:04

insight is what we call them. No.

9:06

Sure. I know I know these days farming is a

9:08

data rich industry. And

9:11

So you have the tractor and the

9:13

data aspect presumably must come

9:15

from sensors on the

9:17

tractor. So tell me about the

9:19

data gathering and then how it can be used.

9:21

We have a number of cameras on the

9:23

tractor. We have both three d

9:25

cameras as well as normal monocular

9:28

cameras. The three d cameras are

9:30

very interesting in the sense they create

9:32

this point cloud data. Very

9:35

much similar to what a radar or a

9:37

light artist where you see points

9:39

you go. But the advantage of three d

9:41

camera is is we get that point

9:43

cloud data along with

9:45

the color image. So now

9:47

our computers can process both those

9:50

together and really

9:52

identify objects, identify

9:55

the path, and all of

9:57

that data allows us not only to

9:59

control the tractor, also we

10:01

can give that data to the farmer

10:03

with actionable insights by saying, hey,

10:05

the color on this leaf is is

10:08

off. You need to send somebody over to

10:10

check it because it might need a pesticide or it

10:12

might need some additional fertilizer, etcetera.

10:14

We make this whole data

10:17

open to anybody, which

10:19

means that a university

10:21

student can write an application

10:23

that will help a farmer around the world

10:26

on coming up with some

10:28

insights on making farming better.

10:30

But we overlay that Garrett with

10:32

additional sensor data including

10:34

GPS. So for example,

10:36

we can answer the questions of

10:38

not only who, what, where,

10:40

but also how. So we can

10:42

collect all this into a structured data

10:44

lake. And we also make that data lake available to

10:46

other third party researchers who can

10:48

provide additional value to the

10:49

farmer. Where do you

10:50

see all this going in the future and

10:53

technology and farming generally. Yeah.

10:55

So we see a world where sustainability

10:57

in terms of how we scale our

10:59

food ecosystem is now top of mind

11:01

for everybody. So

11:03

that's an important aspect. Farmers are

11:05

now struggling not only with weather,

11:08

with

11:08

the shortage of labor on their farms.

11:10

They're also having to meet sustainability

11:13

demands from state and federal

11:15

agencies around the world. But the

11:17

beauty of it, Garrett, is and something that I'm

11:19

very excited about is for the

11:21

first time, I think, we as consumers,

11:23

you and I can actually see how our

11:25

food was grown and have a direct

11:27

connection to the

11:29

on farm operations that

11:31

went into putting the food on water table.

11:34

Right now, we know more about the

11:36

delivery person who tell you what our food than the

11:38

farmer who grew the food. Right?

11:40

So we want to fix that. And I think the

11:42

future is going to be amazing

11:44

where we have these very

11:46

customized, very localized nutritious

11:50

food available at scale for the world's

11:52

growing population, but grown in

11:54

a sustainable manner both from a

11:56

side and planet standpoint as

11:58

well. That's Praveen Penmetze.

12:00

So Bill Thompson, one thing that struck

12:02

me in that interview with the amount

12:04

of discussion about data, like the lovely

12:07

point crowd data from the sensors on

12:09

the camera, you know, three d flip

12:11

mapping of where it's

12:12

been. Yes. I mean, they're

12:14

they're collecting so much data that that could

12:16

be of enormous value just just in

12:18

terms of sort of reshaping the way people think

12:20

about, you know, the way food is grown.

12:22

There there's always a question about, you know, how you would

12:24

get access to that data. Whether people

12:26

would actually be interested in it? I know that

12:28

a lot of organizations have tried to do things like

12:31

trade disputes. Clothes and stuff like that.

12:33

And frankly, people,

12:35

consumers don't seem that interested. They sort of

12:37

like to know it's there, but they don't actually go and

12:39

investigate it. But this does

12:41

offer certainly authorities and regulators and

12:43

sort of the people who want to know that

12:45

food has been properly grown, access

12:47

to, in fact, superior information what's

12:49

been going on on the farm. I

12:52

would hope that whatever data is collected is

12:54

also available to the farmers themselves

12:56

using other ways it's like I'm really

12:58

focused invested in open data and how

13:00

farmers might find ways to exploit

13:02

or use that data that haven't been

13:04

thought of by the manufacturers as well.

13:06

Yes. Because I I think Praveen was talking there about

13:09

actionable insights, for instance, you

13:11

know, from the the

13:11

data, so one would hope. But

13:14

there's always this thing about in such

13:16

once you put these complex automation

13:18

systems in place and let's assume they can

13:20

be maintained and sustained going

13:22

forward. Where control

13:24

lies. There's some

13:26

controversy in the United States, particularly

13:28

about the tractor manufacturer

13:30

John Deere, claiming that the software that

13:32

runs its tractors, it belongs to

13:34

it is its copyright. And therefore,

13:36

farmers can't, in any sense, sort

13:38

of, reconfigure it. And

13:40

that's quite a controversial point of view because it's

13:42

kind of asking who owns the machine

13:45

and who owns the software that runs it? And

13:47

I think what we need to be looking for

13:49

is it a situation within which

13:51

these complex machines and the fact

13:53

are seen as a partnership between the

13:55

the owner of the machine and

13:58

the creator. To work collaboratively,

14:00

but it's not a question of locking

14:02

farmers out of access to the data they

14:04

did collected or indeed locking them

14:06

out of the ability to maintain

14:08

technology. They absolutely rely on to

14:10

harvest their crops. Well, I'm sorry,

14:12

Bill, is it just to make it familiar

14:14

to listeners outside agriculture. For instance, not

14:16

being able to change the battery in your smartphone.

14:18

Well, that sort of example, but actually, it's more not

14:21

being able to use third party printer

14:23

cartridges because your printer identifies

14:25

a cartridge that's being purchased from

14:27

the manufacturer and won't run with any other

14:29

one. We see this constant, if

14:31

like, tension between the

14:33

people who make himself and quite reasonably want to

14:35

make money out today and want to work with it,

14:37

and the people who own the devices about

14:39

who can maintain, who can service, who can

14:41

change it. And as we move forward,

14:43

it becomes more sophisticated. I think it's really

14:45

important we have this discussion and debate

14:47

in the open so that people

14:49

don't end up signing up for a service they don't

14:51

want to use. And and I'm sure that, you know,

14:53

Praveen and others would want to have something which the

14:55

farmers find a positive experience in their

14:57

lives and not something which is difficult for

14:59

them. Alright, Bill. Thank you.

15:01

So with the help of a driver

15:03

optional electric tractor,

15:05

The crops are planted and growing. Our

15:08

next problem is controlling pests,

15:10

and that's where we go now

15:12

with a smart Insectract, it

15:14

gives the farmer a real time pest

15:16

survey by capturing insects

15:18

and identifying them through deep

15:20

learning. What's a likely facial recognition

15:22

in your phone's photo library, you want to

15:24

get the pests, not the pollinators, so

15:26

the trap helps to better target

15:29

pesticides rather than spraying

15:31

chemicals or whatever, everything that we ate.

15:33

Pest destroy up to forty percent of

15:35

global crops and cost two hundred and

15:37

twenty billion US dollars worth

15:39

of losses worldwide annually

15:41

according to the Food and Agriculture Organization of

15:43

the United Nations, the FAA.

15:45

Well, matter Stefancik is the

15:47

chief executive officer of

15:49

Trapview, the Slovenian company

15:51

behind this trap. He wants to move away from

15:53

what many farmers are doing at

15:55

the moment.

15:56

What really happens is, I know that

15:58

simplicity always wins.

16:00

And at the moment, the most simple,

16:02

the most common way of

16:04

of dealing with best insects is

16:07

basically eliminate. However, that

16:09

causes quite some issues. Right?

16:11

You you run into resistance if

16:13

you're over pray. The residual

16:15

levels in in the food we are

16:17

eating is relatively high.

16:19

And that has been one of the

16:21

issues that is being tackled with with

16:23

new technology. Right? So how

16:25

can we better understand

16:27

what's happening with the pest insects?

16:30

How can we predict how they

16:32

will develop. So we can use

16:34

software products. We can use maybe more

16:36

biological crop

16:37

protection. Which brings us to

16:39

your solution. It's called trap you

16:42

say, tell me a

16:42

bit about how it works. Collecting the data

16:44

is a big portion of it. And for that,

16:46

we developed automated traps, which

16:48

are catching insects with

16:50

chard. Taking picture of what was

16:52

caught and sending this data

16:54

over the cellular network to the cloud

16:57

to really get rid of

16:59

this reliance on the

17:01

manual work to collect the data.

17:03

The next step is then what you what you

17:05

do then with this row data,

17:07

right, with pictures. And that's how

17:10

you do the processing with image

17:12

recognition. That's how you do the machine

17:14

learning forecasting of what will

17:16

happen with the past population in the

17:18

in in the

17:18

future. So this is automated in the

17:21

sense that you have pheromones that attracts

17:23

the And then you get I mean,

17:25

you'll tell me, like, hundreds or thousands

17:27

of insects over a period of time. And

17:29

then through image recognition across

17:32

the cloud, you can identify which

17:34

insects and which and then get a

17:36

snapshot as to which ones you need to

17:38

worry about and so on. So that's it's like an

17:40

insect survey that's automated.

17:42

How about the image recognition

17:43

though? How do you discriminate between the different

17:46

insects? The real technical

17:49

question we are dealing with is

17:51

how many of the males of

17:53

that species we are looking at

17:56

have been caught because

17:58

that's how you build the information about

18:00

what's really the best pressure and

18:02

how it differs from different locations.

18:06

And the extraction of this

18:08

information from the pictures themselves

18:10

comes in two steps. The first one is

18:13

image recognition. Here

18:15

we have really strong position because

18:17

we built the biggest database of

18:19

pictures of insects in the

18:21

world that that really allows us to use frameworks

18:23

or techniques like

18:25

like deep learning very efficiently.

18:27

And most people might

18:30

know this from how the

18:32

cars recognize the traffic

18:34

signs or how it brings them closer to

18:36

the autonomous driving. But

18:38

it's basically the same technology in

18:40

the background that we use to

18:43

identify the targeted insects

18:45

from the picture, which could be

18:47

really obfuscated, you know, with

18:49

leaves, with

18:50

debris, also with other insects that

18:53

have been caught One issue

18:55

here though is you're using mobile

18:57

data to, you know, get the images from

18:59

the trap into the cloud where they can

19:02

be processed. But farms by

19:04

their nature are often in remote places. And I'm

19:06

just wondering around the world is a

19:08

lack of mobile

19:09

connectivity, a deal breaker for how this

19:11

can all work.

19:12

Sometimes you have pure

19:15

mobile connections like Europe

19:17

is very well covered.

19:20

And of course, another part is is done in some

19:22

sort of a hybrid mode, you know,

19:24

where you would have cellular network

19:26

in an area, but those

19:29

sell towers with connect to our satellite

19:31

to send the data. So

19:34

connectivity, yes, in some areas, it could

19:36

be a a challenge. But

19:38

that is something, you

19:39

know, that that has been much more

19:41

of an issue in the past than it

19:43

is now. If you're gathering effectively

19:46

like a real time survey

19:48

of insect populations across

19:50

swathes of

19:51

land. How does that information help the

19:54

farmer? Consider this case. Know,

19:56

you have coddling mouth

19:58

on apples. And you

20:00

want to use some, let's

20:02

say, natural predator, some

20:05

other insect or or

20:07

species that is eating eggs

20:09

of the cuddling moth. You

20:12

cannot use it when the best has already developed,

20:14

when you already have Jarvis, when it

20:16

already went inside the Apple.

20:19

So it's very important to use this kind of

20:21

real time data and real time

20:23

or and localized data.

20:26

With accurate prediction of how the best will

20:28

develop. So you can use the right

20:30

product at the right time. In some

20:33

cases, It is eggs or early

20:35

stage larvae. Yeah. In some cases, it

20:37

could also be adults, but more

20:39

and more of a softer are

20:42

really targeting early stages of development

20:44

and that is before the

20:46

damage has happened. So it

20:48

is really very much about

20:51

prevention of the problems to happen and

20:53

reacting on time.

20:55

So moving yourself away from

20:58

being, you know, reacting to

21:00

the problems to to

21:02

preventing them.

21:03

That's Matteo Stefanciets. So

21:07

Bill Thompson, What about this aspect

21:09

of using machine learning here

21:11

in identifying the insects as part of

21:13

that proactive approach that we

21:15

just heard there from Cartag.

21:17

At

21:17

last, a positive use of facial

21:20

recognition. You know, insects don't have

21:22

data rights and therefore, it's fine to

21:24

identify them in your trap. I have to think

21:26

given all the controversy we have

21:28

over ML being used in wider

21:30

society, it's nice to see what clearly

21:32

has positive uses. And

21:34

I think that building this database

21:36

and being able to sort of possibly identify

21:38

the insights is going to be challenging

21:41

and trap you would be working for some

21:43

years. But it's absolutely a solvable

21:45

problem. But you, of course, put your finger on it

21:47

with your question about the the WiFi and the

21:49

connectivity and things like that, which is all

21:51

of these systems don't exist in

21:54

isolation. It's not that, oh, look, I've got this box

21:56

and it's got a fair amount of crap and it's

21:58

identifying the insects. And therefore, it's going to be

22:00

great. How does that information get

22:02

into the system to be used and

22:04

exploited. And you can't think of any of

22:06

these things in isolation. And I

22:08

like the fact that the the trap are

22:10

thinking about that broader context. Right? It's not

22:12

just we've got this cool little box, you can put it

22:14

on the tree. It's why is the overall system

22:16

that will be used to positively

22:19

help

22:19

farmers? Alright. And I need to spend a

22:21

lot of time just checking for bugs

22:23

in the software. And, Jake, there for the tech

22:25

people, but thank you very much here all

22:27

night. Okay. Moving on. Thanks, Bill, by the way.

22:29

So we've sown the

22:31

crop with our electric tractor. We

22:34

protected it with that smart insect trap

22:36

with its machine Now it's time to

22:38

harvest, but with a growing

22:40

shortage of workers waiting to do the

22:42

poorly paid back breaking

22:44

work, how can robots help?

22:46

Machines already do all kinds of harvesting,

22:48

but soft fruits needing a

22:50

delicate touch while they remain a challenge.

22:53

In Portugal though, a robot is now harvesting

22:56

raspberries. It's been developed in

22:58

Britain and the tasty fruits of its

23:00

labor could soon be on our

23:02

supermarket shelves potentially. Here it

23:04

is in action on a farm

23:06

in Odamira in the Southwest

23:08

of Portugal. And if you're expecting

23:10

it to be some kind of robotic hand

23:12

with fingers, then think

23:14

again.

23:16

Hi. We are in Portugal where

23:18

the robot is inside one of

23:20

the lines with brush pens at

23:23

Summerberry Farm and

23:26

Right now, the mobile base is moving

23:29

to kill it detect a barrier. A

23:31

barrier has been detected. The

23:33

arm is now moved to underneath it.

23:36

It has inflated

23:39

a plastic membrane and

23:41

after it pulls the berry and

23:44

then goes to deposit inside

23:46

of a

23:47

panicked. That's

23:49

Andre Martin. He's a field test

23:51

engineer with fieldwork Robotics.

23:54

Andre's colleague, academic founder,

23:56

and chief science officer, Martin

23:58

Stollen, is the brains behind

24:00

this robot. If you see the machine in

24:02

the field is maybe about the size of

24:04

AAA large kind of

24:06

American refrigerator on wheels, If

24:09

you can imagine that, it

24:11

has four robot

24:13

arms on it. And

24:15

it's basically is able to

24:17

navigate between the rows of raspberry

24:20

plants and then it stops

24:22

and picks out targets and

24:25

carefully picks one

24:27

rasp at a time and puts it into

24:29

pellets.

24:29

The mobile base is

24:32

moving till it finds another barrier

24:34

to

24:34

pick. It has found one.

24:36

The arm is gonna position

24:39

itself underneath it.

24:41

There are cameras at the end of the arm to

24:43

make sure the berries inside

24:45

the

24:45

cup. It's

24:48

moving

24:48

up. It's inflating the

24:51

membrane. It's pulling

24:55

and now it's going for another

24:57

berry.

24:57

And so the robot goes inflating

25:00

and deflating its cup like hand

25:02

as it works its way through the

25:04

crop. Martin Stolen says it's taken a whole load

25:06

of trial and error to refine

25:09

the design. Being a technology

25:11

that's in heavy development, we've gone through

25:13

quite a few different iterations of

25:15

QuickBooks. And

25:15

actually, the latest version of gripper

25:18

looks a bit more like a

25:20

cup that approaches the

25:22

rasp free from below and it then

25:24

inflates a membrane

25:26

around the raspy to to

25:28

gently squeeze it and be able to pluck it off

25:30

the plant. Right. So

25:31

this is the latest iteration then.

25:33

So you've tried the finger pincer

25:35

movement found maybe it's not quite

25:37

delicate enough. So now you have the membrane

25:39

almost like a is it like a bubble or

25:41

most it? You know when people blow bubbles

25:43

with gum. Isn't that kind of bubble, if you

25:45

like? Yeah. Just I guess That's

25:47

the rubbery. I guess

25:49

if you can imagine Well,

25:52

kind more like a doughnut, to be honest.

25:54

As you

25:54

imagine the raspberry entering into a deflated

25:57

doughnut and then the doughnut inflating around

25:59

it to even see the raspberry in the

26:01

first place. There is a vision system

26:03

on this robot, isn't there? So perhaps tell

26:05

me a bit about that. However, even knows to

26:07

pick a raspberry and not just pick a

26:09

leaf off the plant or something. Correct. So the the the

26:11

first challenge you have is to to

26:13

try to to pick out the

26:15

Rasprey from the surroundings. And

26:18

for that, we we

26:20

use cameras, of course, and we use different

26:22

types of cameras, cameras

26:24

that look in at colors. So

26:27

the color of the raspberries is a good

26:29

indication of how or where it

26:31

is compared to the leaves. But

26:33

also cameras are able to detect the

26:35

three d structure of the bush itself and the

26:37

raspy in relationship to it. And that enables

26:39

us also to pinpoint the

26:42

location of any potential targets,

26:44

any potential raspberries. And then we

26:46

have machine learning

26:48

algorithms that enable us to say

26:50

something about the maturity of that

26:52

recipe. If it's ripe for picking or not, if

26:54

it has the caesars, etcetera. How did it

26:56

take to develop the robot? Fumor Robotics

26:58

was spun out of the University of Plymouth when

27:01

I was I was a lecturer there and I was

27:03

back in two thousand sixteen.

27:06

So we've been working on it heavily since then.

27:09

We've of course been growing, started out

27:11

as a small typical spinout

27:13

company. We have some support from a

27:15

company called front TRIP that helped

27:17

us kind of get going. And

27:19

then we've gradually grown to about

27:21

fifteen employees now. And

27:23

we are at the stage where we now have

27:25

a robot in the field continually

27:27

in Portugal that is

27:30

partially used as developing a

27:32

new technology and partially demonstrating

27:34

that we can generate revenue. How

27:36

does the robot compare to a human

27:38

picker? There's four

27:39

robot arms on each platform, and

27:41

each robot arm you would say it's a

27:43

bit moving a bit slower than than a human

27:46

arm at the moment, but you in

27:48

sense recuperate that by having

27:50

more arms by operating much

27:52

more hours, you can operate almost twenty

27:54

four hours a day. And

27:56

also the key point for us is

27:58

that you need to look at the cost of the

28:01

technology. So if you compare

28:03

the technology with

28:06

robotics, that we put into the field,

28:08

the requirement for those robots

28:10

are quite different from the robots that you would

28:12

have in, say, a factory. In

28:14

a factory, you might need

28:16

very fast speeds. You

28:18

might need a sub millimeter accuracy.

28:22

Which are things we don't necessarily need

28:24

in robots that's going to pick raspberries.

28:26

So also then the costs come down

28:28

considerably. And where we compare

28:30

ourselves to the human

28:32

harvesters is really on the cost

28:34

per barrier or the cost per

28:36

kilogram, which also is

28:38

how

28:38

a lot of the business model around the harvesting robot

28:40

is centered on providing them as

28:42

a service. Yeah. I was wondering about the

28:45

economics of it. So

28:47

you'd say that there it is viable then

28:49

because these are not cheap, but you lease them

28:51

out, and then that gives a a good deal

28:53

to farmers or at least that's your

28:55

plan? Certainly. And we're already demonstrating

28:58

for the first time this year, we were able

29:00

to harvest dry sprees that

29:02

passed the quality control of one of

29:05

the largest raspberries growers in the U.

29:07

K. Which also has operations

29:09

in Portugal. And

29:11

demonstrate that we could sell these berries.

29:13

They're they're picked to spec. And

29:15

we are now gradually

29:18

rolling out more robots in the field and each

29:20

robot is gradually picking more

29:22

and more kilograms per

29:24

hour and we're quite confident that we will

29:26

be able to have

29:28

profitable operations not

29:31

in a very distant future. Yeah.

29:33

There you go. That's Martin Stolland.

29:35

We also heard that

29:38

from Andre Martin's.

29:40

Bill Thompson, I suppose there's a bigger question here

29:42

though, isn't it about where we want the robots to

29:44

stack in and maybe where we want to keep with the

29:46

human pickers or

29:47

growers? Or tractor they make it.

29:49

I think there always is,

29:51

Gareth, absolutely, that

29:54

with any of these attempts to

29:57

automate processes which have been traditionally carried

29:59

out by humans, we have to ask

30:01

ourselves, is it a job we want people to do?

30:03

Are we happy have it handed over to

30:05

machines. And what does that

30:07

do to local economy? There's absolutely

30:09

an issue that fruit picking isn't a

30:11

particularly rewarding job. It can be physically

30:13

very intensive and quite

30:15

damaging. But if it's the only job people

30:17

have, maybe you want it

30:19

to be still available to them. And there's

30:21

always that balance. It's

30:23

clear from just the description of the

30:25

technology that we're moving to the point where

30:27

these robots or similar robots will be able to

30:29

do this job in particular

30:32

contexts. And again, it comes back to this

30:34

point again. They have to be embedded in

30:36

quite complex systems. They need to be

30:38

maintained, you know, apart from things like

30:40

power supplies and charging. They need to

30:42

be repaired. They need to be

30:44

supported. And so there are only going to be a limited number of environments

30:46

within which they are absolutely

30:48

viable. That will be countries with a

30:50

very well developed industrial infrastructure build

30:52

to provide that support. The real

30:54

danger would be to try to offer them to

30:57

places where they would break down very quickly

30:59

and end up being useless. I I

31:01

think that looking at the development of

31:03

this picking technology, has that been

31:05

remarkable? And just the innovation in terms

31:07

of using, as we've described, the membranes and

31:09

other things to do the picking, The

31:11

fact that the robot hands are three d

31:13

printers and effort can be adapted to

31:15

to different produce over time. All of

31:17

that, it's a really brilliant engineering

31:20

project. But always the question

31:22

is, what's this engineering offer to

31:24

the people like the farmers who actually

31:26

need to get their job

31:27

done? Is it good for Alright. Thank

31:29

you very much. He's Bill. I'm Gareth.

31:31

The producers are Allan

31:33

Beech and also Andy Litterover. It's

31:35

the studio manager. It's Steve Greenwood. See

31:37

you soon. Bye bye. Alright. Well, let

31:39

let's carry on and we started the pod

31:42

intro with some New Year's Tech

31:44

Resolutions. And, of course, that was

31:46

all very positive and

31:48

forward looking. So I just think, Bill,

31:50

we may as well just go back to a bit of

31:52

tech winching which is the strength. I think we

31:54

nearly killed up before

31:54

Christmas, but there were just a few good ones, so I'm afraid I'm

31:57

gonna use them today. You you said

31:59

you were gonna come back to this. This is this is

32:01

turning into the marble calendar of

32:03

the nose. Period of our existence,

32:05

isn't it? Cara, please

32:07

share some tech nineties with us. I'm

32:10

fascinated and intrigued to know what

32:12

they might

32:12

be. Oh, thanks for reading that thing I put on the

32:14

script there, that note. Thanks, Bill. Right

32:16

on script there. So alright.

32:18

Then since you insist, Bill, we can have a bit

32:20

of Richard GaN. This is by the way,

32:23

folks. Just a quick reminder if you can

32:25

bear it that I having problems with

32:27

my broadband before Christmas and

32:29

as well as the funny moment

32:31

really when I was

32:33

on the line to the call center and saying, you know, I think

32:35

I've got problems and they said, yes, we do

32:37

have issues with the broadband in your area. And

32:39

there was, like, cable

32:41

that was cut, and I could literally see it

32:43

hanging loose just outside my door. So that's the

32:45

context I just said to you, dear,

32:47

listeners, just if there's anything else that's

32:49

been getting you down then we're nice

32:51

friendly people share it with

32:53

us, and at least we'll listen. So

32:55

but Richard G hadn't not so

32:57

much a tech solution actually, which is wonderful

32:59

and kind of waiting into my

33:01

broken cable kind of issue. He says, well,

33:03

you need a fiber spice

33:06

kit for your next birthday. It's a good

33:08

suggestion, Richard. But my

33:09

provider, I think I might need it.

33:12

Share it with my neighbors on the street

33:14

as well. Yeah. just get in touch

33:16

with Chris Condor at Baum

33:18

brought down from the rural North, which which just

33:20

goes around lain fiber in the north of

33:21

England. They they can help you out

33:24

with that. Oh, alright. I'll I might I

33:26

I may have to do that. And

33:28

I because I wonder what it's like to slice

33:30

a piece of fiber. I've never done that if

33:32

it's sounds quite tricky to

33:34

me. I bet you need to be quite

33:37

good, maybe not. Anyway, let's Can

33:39

I can I Here's what I'm right. They came from me over

33:41

the festive season, which is

33:44

like why oh, why is it so

33:46

hard to install Internet of

33:48

Things devices? as an

33:50

example, my mother has

33:52

a smart voice a voice assistant as

33:54

I think I might have slipped into the

33:56

end of year last week.

33:59

And she loves this voice assistant, and

34:01

so there's a bit of it where you can switch

34:03

the living room lights on and

34:05

off. And I've I've installed

34:07

some smart when she first

34:09

had this voice assistant and everything was working okay. But then

34:11

her good old fiber broadband

34:13

provider then went and changed her

34:15

router so now of course nothing

34:17

in her house works anymore. So I've had to had to spend a load

34:19

of time when I could have been celebrating the festive

34:22

season reconfiguring a load of stuff in her

34:24

house. But it's just putting in

34:26

smart plugs where you have the

34:28

voice assistant, which needs to

34:30

interface with a native app

34:32

that goes with the particular

34:34

make of smart plug.

34:36

And then In order to get the

34:38

smart plug working, the

34:40

broadband router because it's on two frequencies. What

34:42

is it? Two point four and five gigs

34:44

or something? Five. Yeah.

34:46

Okay. But you have to split the

34:48

band. Otherwise, the smart plug might

34:50

try and substitute into the five gig one, which

34:52

doesn't work for it. So then you have to switch off the

34:54

five gig and then just use the two point four. But then I

34:56

found when I did that because I was trying to configure for my

34:58

laptop. Of course, my laptop no longer

35:00

wanted to communicate with

35:02

the router.

35:04

So then I went through my mobile broadband. And about halfway through this, I thought,

35:06

why am I doing this? Why is it so hard? It

35:08

shows surely now with the technology we

35:10

have in the world at the moment.

35:13

We should be able to just buy an off the shelf

35:15

smart plug, plug it in, and then set

35:17

up some command on the voice assistant, and

35:19

it should just work. Why is it so

35:21

painful? Don't need to answer. I just want to get that off my chest. It's my

35:23

other tech win beginning

35:26

twenty twenty

35:26

three. I can

35:27

move on

35:28

if you will. Okay. And very briefly. I mean, the

35:31

answer is there is no effective standardization

35:33

yet. Other people have been working on it used

35:35

to be Zigbee, for example, report. So

35:37

there isn't there isn't a way of doing it. is still broken

35:39

in many ways. We haven't solved that

35:41

problem. And partly it's because the device manufacturers

35:43

want to innovate really

35:46

fast. And so they want the devices to

35:48

do things that aren't necessarily supported by current standards and so they implement something on top

35:50

of it. And so we've got this constant

35:52

tension between innovation and standardization. With

35:56

the router changing problem, in the past when

35:59

that happened with me on a smaller scale,

36:01

I got the new router

36:03

and changed its identified such

36:05

as an ID and password the same as the old

36:08

router. And then things just clicked the

36:10

new router and couldn't realize that it'd

36:12

been changed. Because the yeah. As long as as long as the WiFi standard

36:14

itself hasn't changed and it's supported by the

36:16

new router, it should work. As when the

36:18

device just looks for a particular identifier,

36:20

it says please can I

36:22

connect? Here's here's some credentials, and the

36:24

zone has got the right

36:25

credentials. It should work. Yeah. It should. It

36:27

should a

36:27

lot of work in that center. I

36:29

I get it. And that would

36:31

include changing the SSID on the

36:34

router as well. Oh, I yeah. Indeed.

36:36

I I did wonder about that. It all sounded a

36:38

bit scary to me, and so I just got

36:40

grumpy for a while. And then my work around

36:42

was just what did I do? Yeah. I just

36:44

switched off the five

36:46

gig to wrap band

36:48

on the on the router. And then the it's sure

36:50

enough the devices went for the two point four

36:52

and I expect it to all back up on the gain into the

36:54

normal configuration.

36:56

And it did work. But, yeah, that's a good point,

36:58

isn't it? Just change the credentials. Thanks, Bill. Yeah. I think

37:01

that's worth a

37:01

try. It's Well, I have to try.

37:04

Even, sir, it shouldn't need to be that

37:06

complicated. I will say it needs to be that

37:08

complicated. Right? I'll give you that.

37:10

Thank

37:10

you. But enough of me, let's have some of the Marty

37:12

says my fiber optics Internet

37:14

randomly disconnects around every forty

37:18

eight hours. And I need to reset the

37:20

modem. So I've decided to reset it every night when I don't need the Internet for

37:22

about a minute. My best

37:26

service provider continues Maddy is

37:28

flaking my apartment and its

37:30

vicinity. Sometimes if I move my phone by

37:32

half an inch, the

37:34

call drops. And if my phone says five g around here, that

37:36

means no cell internet.

37:38

And Monday goes on a bit of a

37:40

gripe about his university's IT

37:42

department that's a bit lengthy, so I

37:44

won't go into it now, but I hope you got it

37:46

fixed with the university there. A few

37:48

winters there. And

37:49

also, the Broken IT and New University

37:51

is a preparation for a

37:53

world work where the IT will be before broken. So so look at the

37:55

product as part of the training they're

37:57

offering. Yeah. Although, Marty's

37:59

actually a teacher, I think, is a professor

38:01

at the university. Okay.

38:03

In which case. Sorry. But no but no please

38:05

don't apologize because I think he can flip that and then

38:07

make that as part of his kind of lesson, his

38:10

life lesson as well as his

38:12

technology

38:12

lesson. Says very grateful students in what I think is mechanical

38:14

engineering. I used to do that

38:16

at a certain remaining unnamed

38:18

university in a certain On

38:22

a certain course explain that the really rubbish IT

38:24

systems were their preparation for

38:27

when they got killed.

38:29

They'll they'll be thanking you in later life,

38:31

I'm sure, or hopefully

38:34

just improving them if they go into that line of

38:36

business. Let's do

38:38

one from Chris, if we have times, as I had a similar situation

38:40

with my Internet provider years ago when I was

38:42

using a third party router. Oh,

38:44

yes. The good old third party router thing,

38:46

Chris. Yeah.

38:48

Thanks for I don't know where Chris continues. I finally found out that the phone

38:50

company sent a query down the

38:52

line to see if users were using a phone

38:54

company provided router, i. E. An

38:56

official one. If they didn't get

38:58

the right response, they throttle or stop

39:00

the service. Apparently, finishes

39:02

Chris. They could do this because

39:04

they provided their router for free

39:07

But my issue with that is it didn't didn't

39:09

have any WiFi. So this must have been

39:11

some time ago. Yeah. I'm

39:14

not surprised I bet a load of providers

39:16

do that. They'll just send some diagnostic when they're down the line

39:18

just to check that you're using their

39:19

kit. And because it it might be some

39:21

liability or indemnity things as

39:24

well. Yeah. It

39:26

might be that they claim they can't provide

39:28

service unless you're using their technology, but the

39:30

sort of people who are competent enough to install their

39:32

own Richard generally wouldn't want the sort

39:35

of service that provided because they were already beyond that point.

39:37

Mhmm. It's a it's it's a tricky

39:39

one as as we as we

39:41

know. Yeah. So What's

39:43

a control you should have over the technologies in your

39:45

home. Yeah. A lot. I think Yeah.

39:48

I I had a feeling you would think that. Yeah.

39:50

Mhmm. Alright. Simon

39:52

says, bear in mind the system the run up to

39:54

Christmas that this would have been written. Simon says,

39:56

does the season to be having a flaky

39:59

home network? Simon says I discovered that plugging

40:01

odd stuff into main power sockets, which have

40:03

a power line broadband network running through them,

40:06

can make the connection go bad. In

40:08

this case, was a smart

40:10

home power monitoring the

40:12

plug, but I'm still suspicious of the

40:14

Christmas tree lights. But if you're

40:16

suspicious of them, my guess, disconnect

40:18

them and see if it's fix the

40:19

problem. God, I don't I don't present a tech program but nothing do I with amazing

40:21

advice like that site over there building. So I

40:23

mean Christmas tree

40:26

lights always used to be the problem when they were incandescent

40:28

lights. Now they're LEDs, I suspect

40:30

they're less of a problem. Yeah.

40:33

I I can see why that Yes.

40:36

I suspect you'd be right. Especially if you get the battery

40:38

powered ones. Mhmm. Anyway

40:41

Well, yes. Well, actually so that it's partly about the power, and then it's

40:44

also just about the interference electron

40:46

interference. Yeah. Yeah. Because because a a

40:48

lot of tiny little lights

40:50

flickering away.

40:52

There's a there's a lot there's a lot of electromagnetic interference

40:54

coming from those. Yeah. I think now

40:56

it's as an RF person. It's not kind of the

40:58

kind of thing I'd want near any

41:00

a high frequency kit. Put it that

41:01

way. I will

41:02

always think who you as an RF person. Thank

41:04

you very much. I'll get the t shirt.

41:06

So I do a pretty person, Garrett.

41:10

But there we are. We both have we're both in our

41:12

clans, aren't we then? We we have our

41:14

tribes. We

41:17

do one that one of the most things seem to be going bizarrely

41:19

well, really, which just on the road, we could just take

41:21

a last one from David before we call it

41:23

today and limp out the studio. David

41:26

says I moved house last working day

41:28

before the first lockdown in March twenty

41:31

twenty. They sent an engineer out

41:33

the next only come as far as the green box,

41:35

but told me that should be enough. When

41:37

that person didn't finish the whole job, they

41:39

were then only able to send

41:41

someone else out to fix it in

41:43

June, presumably after you could then enter people's houses again. In the

41:46

meantime, David says he had to run

41:48

his phone,

41:50

burning hot using it as a

41:52

WiFi hotspot in order to keep his

41:54

job, utterly ruining ruining

41:56

the plane's battery, but not his

41:58

career. So David probably saw it as a trade

42:00

off worth doing.

42:01

Yeah. Recovery.

42:02

Yes. Yes. You need. Not bad

42:04

at all. Alright. Well, I'm I think that

42:06

might do it, mainly because I've run out of listener comments.

42:09

So without them, it's just you and I chatting away

42:11

and that might be too much for anybody to

42:12

bear. That that would and that could fill

42:15

quite a lot of time really. But let's not

42:17

inflict that on anyone

42:19

particular producer any no, that's true. I think we've got another four minutes of

42:21

studio time if you do want to fill, but I suspect we should

42:24

just wrap it up. And you're

42:25

agrees. Yep. There we are. But

42:28

I've had his voice. It's

42:30

been a life. It's been a joy. Your

42:32

technology winches have illuminated

42:35

my life. Thinking about in the air in such a

42:37

great way. Oh, you guys. Alright.

42:40

There we are. There'll be more

42:42

sincerity at the same time

42:44

next week. Here on the

42:46

digital planet podcast. Thanks for bearing with

42:48

us folks. And, yeah, see you

42:50

next week. I hope

42:52

cheers. Bye.