December 11, 2024 • 27 mins
In this episode, we explore the cutting-edge innovations shaping precision agriculture. We dive deeper into ADMS with software developer Travis Yeik, talk turning digital signals into hydraulic actions, and dive deep into artificial intelligence (AI).
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Episode Transcript
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Speaker 1 (00:00):
And now it's time for a Geek Speak with GK
Technologies, Sarah and Jodyfriends and I can't wait to get
in the fields again.
Speaker 2 (00:17):
No, I can't wait to get in the fields again.
Speaker 3 (00:31):
Welcome back to the second part of our conversation
with the man behind the curtain,mr Travis Yike out of Wyoming.
We are so happy to have himback here on the podcast to talk
about and geek out about somany different things.
I'm not even going to list himhere because, as you'll see in
the episode, we're going to talkabout a lot of things, but it
(00:54):
is such a pleasure to haveTravis here.
Speaker 1 (00:57):
I think the last time where we left off we left off
with Travis had just gottenhired by Darren off with Travis,
had just gotten hired by Darren, didn't even have to turn in a
resume to get hired, which iswell shouldn't be surprising if
you listen to that whole episode.
He's done a lot of stuff withremote sensing, so let's talk
about this.
What exactly do you do at GKTechnology right now?
Speaker 2 (01:18):
That is a good question, and sometimes I ask
myself that every day, you andme both.
And sometimes I ask myself thatevery day.
Yeah, I've been involved inquite a few of the projects that
we have at GK, obviously theADMS software, which is our main
desktop software and it doesall of our precision ag and GIS
coding.
I'm also involved in our SDdrain and SD ditch and SD tile
(01:44):
and you would probably hear on aprevious podcast Paul Fuller
talked about some of our SDstuff and how we're doing there,
and so I went from one end ofthe spectrum working with valley
irrigation in places thatdidn't have enough water to
places that had too much water,and this is a totally foreign
concept to me from a guy fromWyoming that you know we were
(02:08):
struggling for, you know eightinches of rain a year.
Speaker 3 (02:12):
Let's remember that I had asked the question of why
Travis had focused on doingzoning for irrigated fields in
Wyoming and his answer was wegrow crops that are under
irrigation.
That's where they're grown isunder irrigation.
Speaker 1 (02:27):
So I agree, I come from.
Speaker 3 (02:28):
Western North Dakota and the concept of tile is just
like.
Why do we want to get rid ofthe water?
Speaker 1 (02:31):
But there are obvious reasons.
I can't imagine a farm thatdidn't own a scraper.
Speaker 3 (02:38):
But anyway.
So again, I mean you've done aton for SD drain.
I mean can you tell us about,like where, when you started on
working on SD Drain, where itwas, and then what came to, or
like where it is now and yourinvolvement with that?
Speaker 2 (02:52):
Yeah, I had to learn everything.
We started with Ditch, and so Ihad to learn everything about
why as you said, why we have toget the water off the field.
Yeah, and which was neat, yeah,which was neat.
The big part of that islearning GPS and doing RTK and
understanding all the codebehind GPS, which is a whole new
(03:14):
concept, and to understand andrelate that to how we implement
that with the actual hardwarewas a real step forward in how
precision agriculture is quiteuseful in that way is to get it
into hands, into the technologythat people can use it.
(03:37):
That way.
Speaker 3 (03:40):
At the end of the day , once we get maps into that
program.
That program is then tellingeither scrapers or tile plows
where exactly they need to be inthe field to get water to flow.
Can you talk more about whatyou have to know and how you get
a computer program to talk toan implement?
Speaker 2 (04:01):
Yeah, maybe I'll go back when I actually first
started school.
I went into civil engineeringand I did that, I think, for a
week, and I was like I don'twant to sit at a desk and do
math all day.
That is all I do now.
Sorry about that.
(04:23):
Sorry about that.
Yeah, so it's a lot oftrigonometry and that's one of
the math subjects I guess that Iwas good at and Kim's back and
haunts me to this day but isdoing that.
And we work with a greatcompany.
We work with Rust Sales andguys there.
They actually make the thatcontrols the hydraulics behind
(04:48):
everything, and I talked to thatDAC to tell it how to where to
send the implement or send thescraper blades up and down.
Speaker 1 (04:57):
So, travis, what is a DAC?
I mean, we talk about this allthe time with our customers that
use, you know, go to Rust salesand get their SD drain systems.
What exactly is that DAC thatgoes in the DAC, in the cab?
Speaker 2 (05:13):
Yeah, so DAC stands for digital to analog control,
and it's what changes from whatI send it as code in digital
form, from what I send it ascode in digital form, and it
changes that to the voltageswhich then converts it into how
(05:37):
much the hydraulics need toapply the pressure to make the
scraper blade go up and down.
Speaker 1 (05:39):
So essentially, it really is the place where you're
sending your computer code andthat's how we're ultimately
voltage first, but then it's thehydraulics.
Speaker 2 (05:46):
It's the little box that makes the magic work.
Yeah, there it is, that's right.
Speaker 1 (05:50):
It's the magic box, the man behind the curtain with
the magic box.
That's pretty neat.
We've had so much success as acompany with SD drain and SD
tile, and part of it is justbecause the system is programmed
so well and it works so welland the farmers love it.
The farmers love getting themaps in the background that work
(06:14):
so well with it as well.
In addition, we have a greatsystem for making surface
drainage maps and a great systemfor making drain tile maps.
Thank you, travis.
I just think it's important foreverybody to know that it
really is quite revolutionary,and Rust Sales does have
incredible customer support.
If you need help figuring outhow those systems work in the
(06:36):
tractor cab, they just haveincredible customer support.
We've been so lucky to partnerwith them.
Now, did you do most of theprogramming on SD drain and SD
tile, travis?
Speaker 2 (06:47):
Yes, yep, so some of the map controls we were able to
reuse those from ADMS, but thenthe rest of it is from scratch
that we put together and it'sbeen in.
How long have we had SD here?
Since 2014, I think.
Speaker 1 (07:04):
It was a long time before I came to work here.
Speaker 2 (07:06):
So it's been out there now for 10 years and I
think that's one of the thingsthat I was concerned, I guess,
or trying to get across withPrecision Ag is that I think
there's a few things inPrecision Ag that we all
struggle with, and that's thecost of it.
Sometimes it can be costly toimplement and especially if you
(07:27):
have like a small acreages oreven, you know, just getting the
cost down so that everydayusers can use it.
And then the second majorproblem is the technology of it.
Is that there needs to be, itneeds to be simplified so that
everyday people can use it in away that's In ADMS.
(07:48):
Don't get me wrong.
Adms is a wonderful product andit takes a lot of skill to use.
There's not an easy button atall in ADMS, but in SD a lot of
farmers they need just a two orthree button clicks to get to
everything that they need to doand I think that's important
with the future of precision agis to make that available to
(08:11):
people.
Speaker 3 (08:18):
I'll just say thank you so much for making it a
simple two, three button clickand SD drain and SD ditch.
Because, absolutely, as apractitioner of precision
agriculture, as a farmer, right,there are so many things that
you need to know as a farmerthat it is very difficult to sit
down and say, hey, I'm going tolearn how to do GIS and
understand you know how allthese maps fit together, and
then go out and do this.
It's just something that when,in the grand scheme of all the
(08:38):
decisions that have to be madeas a farmer, when it's like,
okay, I've got to take a coupleweeks off to learn how to this
completely new concept, it'sthat you're right, that cost,
it's a big barrier.
So the simplicity of it is huge, and that probably took a lot
of time on your end and thinkingabout how do I make this
process that's agile enough,that can be done, and two or
(09:01):
three button clicks.
So thank you for taking thetime to think about that while
you were developing and puttingtogether those systems.
Speaker 2 (09:10):
And it's probably more of the reason that or just
that it didn't come from adrainage background is that, you
know, I had to understand itand come back through it, so I
wasn't able to look at what wasout there already.
And it's and I think maybethat's another reason of the
success for it in trying to dumbit down is that I was
(09:31):
understanding how the drainageprocess works at the same time I
was developing it.
Speaker 3 (09:36):
That's really interesting, right?
Because a person would thinklike, oh, you'd have to really
know how these other programswork in order to make our own.
But your point is is thatbecause you you didn't have like
a quote-unquote bias of how thesystems already worked, so you
could, you know, make it yourown with the mind of simplicity
as or like, with the point ofsimplicity being your end goal?
Speaker 1 (09:59):
yeah, that's huge.
So question for you um, in theADMS software that we have, we
have the drainage window andwe've got watershed modeling and
we've got tile design.
Did you program those or didDarren program those?
Speaker 2 (10:16):
Darren, yes, we originally had a drainage and a
tile and Darren had done a lotof that in watershed modeling
and Darren had done a lot ofthat in watershed modeling and
when we moved over to the SDproduct I redid a lot of that
and we've had two different nowdrainage models in ADMS.
You know, that interesting kindof looking at the research
(10:38):
articles and putting researcharticles in the code was
difficult but it's fun at thesame time.
Speaker 3 (10:46):
This is kind of a weird question.
Do you have a favorite researcharticle that you reference a
lot or think is written verysimply?
Speaker 2 (10:55):
No, not a favorite.
There's so many researcharticles and they cover so many
topics, as does ADMS covers somany topics, and so it's hard to
pinpoint one down.
I guess, as I go into some ofthe new stuff that I'm learning
with artificial intelligence andwhatnot, that a lot of those
articles I could not find hardlya single article on using AI in
(11:19):
precision agriculture.
A lot of it is looking at,maybe like the USDA on saying
hey, this is soybeans and thisis corn, and doing a broad
spectrum of average and using AIfor that.
But as for more of theprecision ag stuff, I had to
scour.
I guess all the money for AI isgoing into technology and
(11:41):
entertainments and into themedical field.
Speaker 1 (11:44):
Interesting.
You're working on a number ofdifferent projects right now in
the background things that aregoing to be coming out into the
future for GK Technology.
Do you want to share with uswithout sharing too much?
What kind of fun new toys arewe going to get to play with
(12:05):
Travis?
Speaker 2 (12:06):
Well, so, as I was saying, part of Precision Ag and
the thing that I think about asa developer in the background
is understanding how to getcosts out lower, I guess, for
people and to make thetechnology widely available
(12:26):
available, and so I so right nowwe're coming out with, with a
phone app like as, where, um youknow, farmers can have
precision ag gis um in in theirpalm of their hand with their
phones and hopefully use it thatway, and in this case it'll
it'll be used a lot for forsurveying and for record keeping
and for sharing data back andforth between our main program,
ADMS, and I think that'll besuper helpful to our customers.
Speaker 1 (12:49):
And soil sampling.
Speaker 2 (12:51):
Absolutely soil sampling.
Speaker 1 (12:56):
We've got to have it in the soil sample rig.
It's going to be a lot of fun,and so if you're looking for
that way that you can seamlesslydata exchange between this app
and your ADMS software, it'sdefinitely something to check
out.
I think it's going to be a lotof fun.
What other fun projects are youworking on right now?
Speaker 2 (13:14):
Yeah, sd Drain is always expanding.
Right now we are testing ashaping product which will be
coming out, hopefully, eitherend of this year or next year,
after we do a bunch of testingon that.
Speaker 1 (13:31):
And that's land shaping right.
Speaker 2 (13:33):
That is land shaping, so taking an entire field and
getting rid of your potholes andmaking it so that water runs
downhill Yep.
Speaker 3 (13:43):
One of the questions I get when I go to my fiance's
home state is about terracing.
Has there been anyconsideration to land shaping to
do like terracing?
Speaker 2 (13:52):
Yeah, absolutely, and I think that's what our shaping
program will be used is forsome of these things like
terracing or creating.
You know if you're doing ricepaddies or we are just leveling
areas off.
Yeah, yeah.
Speaker 1 (14:07):
Because really the focus of SD drain and into
itself, when we think aboutdrainage, surface drainage is
just kind of to get rid of thatpothole that's out there, right,
and get the ditch clean.
But whereas with land shapingyou are, you are literally able
to create a dam.
If you need a dam in a place orget the water to flow, you can
(14:29):
literally shape the land.
Speaker 2 (14:32):
Or a golf course.
Speaker 1 (14:33):
Yeah, there we go.
I like that.
That's pretty fun, so that'sgoing to be something that's
going to be new.
Coming out is land shaping.
Speaker 2 (14:56):
You've got that app, so you're working on all kinds
of fun projects then Anythingelse use, whether it's copying a
politician's voice or creatinginappropriate images or whatever
it is, and there is some tabooaround it, but I think there is
a lot of potential in the futurethat we can use artificial
intelligence in precision ag tohelp us make better
(15:19):
decision-making processes.
Part of the problem withprecision ag, I guess, is that,
yeah, there are a lot ofvariables, right.
We have soil data and thequality and the types of soils,
and we have you know how thatrelates to fertility of the
plants and the soils themselves,whether it's nutrients or water
(15:41):
management, and then we gotenvironmental factors such as
rain or hail, or pests or weedseven, and so agriculture has a
lot of variables and and there'slike disciplines, right, that
like focus in on each one ofthese variables and to expect a
farmer or a consultant oranybody rather to to know any
(16:02):
and all of this stuff is you'vegot to have four or five
different guys in the field todo that, and then to wrap that
all up into a precisionagriculture software package
that people can utilize andunderstand is difficult to do as
well.
Intelligence may be a bridgebetween all of these different
(16:24):
variables, to understand andmake sense of some of these
variables that are hard to model.
Speaker 3 (16:32):
So GK sent you back to school, right?
Speaker 2 (16:36):
Actually, when I was in undergrad there AI is not a
new thing they're calledMarkovian decision models and
these were created back in likethe 1960s or so, and so AI has
been around for a long time.
Even when I was in undergradthere, they had computer
programs for remote sensing thatcould model and do
(16:58):
classification coverage using AI.
It's only within like the lastI don't know even five or eight
years that it's become popular,and I think some of the, the,
the coding Google has some codeout there that's open source,
that that the everyday publiccould use and and and to
(17:20):
understand and create their ownAI models that way, and and then
now we have ChatGPT and Geminiand all these other things.
That AI is kind of blown out ofproportion now, but so it's
been out there for a while andwe're just now starting to
understand and utilize it betterin our everyday practices as AI
(17:40):
develops more and people getmore interested in it.
Speaker 3 (17:44):
So what exactly is artificial intelligence, ai,
what exactly this is and whatdoes it mean when we say
something is AI?
Speaker 2 (17:52):
Yeah, that's a good question and it's hard to.
One of the things I learned inone of the internships I had
with the Department of Energywas to create a grandma speech
right.
This is the speech that youtell your grandma when she asks
you, hey, what do you do?
And you have to break it downinto terms that she can
(18:14):
understand it right.
Speaker 3 (18:15):
I love.
I'm sorry, I love.
It's like you learned about thegrandma speech working for the
DOE.
Speaker 1 (18:20):
I just like that a lot.
Speaker 3 (18:22):
I get a kick out of that, anyways.
Speaker 2 (18:25):
And so I guess here's the way I would, I would define
ai.
Let's take a giant plinko gameand you have a different maybe
you got a little round ball atthe top and you drop it down and
it goes through all thedifferent pegs, right and uh.
And then at the bottom maybe iswe have, uh, you, our bins at
the bottom, and these are kindof like our decision making
(18:47):
process, right.
And so as we make a decision,as we sit here as we talk or as
we think about a solution or aproblem to something, or problem
or solution to something I saidthat backwards that we have a
Plinko that goes through and itgoes through all of our
different nerves in our head andit bounces off of each of the
little pegs and finally it comesdown to the bins at the bottom
(19:08):
where we make a decision.
And so as we AI is I'm going tomake the analogy here that if we
change the maybe the diameterof each of those little pegs in
that Plinko game and we roll itdown, that it will we can make
it so that that little ball canroll in the right bin that we
(19:29):
want it to.
And AI is learning that.
Hey, if I change this way ofthis peg differently than this
peg, then we can make it rolldown into the right bin and we
have different inputs.
So we may not even have acircle, we may have a square or
an octagon or some geoid that weroll down the Plinko and after
(19:52):
a while we could have a milliondifferent little pegs that it
can, which is all the variables.
So some of those variables Italked about, such as soil or
water, nutrients or weeds, andif each one of those models was
talked about, such as soil orwater, nutrients or weeds, and
if each one of those models wasa different little pig that it
could roll differently based onhow that geoid was shaped, and
roll it out into the right bin,does that make sense?
Speaker 3 (20:15):
I think the way I'm understanding this is that
you're giving the right amountof weight to different
explanatory variables to accountfor more of the variability
that affects an outcome, andyou're accounting for more of it
.
I think AI is.
Speaker 1 (20:35):
I think your description made sense and I
think AI is going to have a fitin agriculture and I think AI is
going to have a fit inagriculture.
I know that there's a lot ofconversations around different
ideas that we can do with AI andI think some of the decisions
that are some of the placeswhere we may need help modeling.
You know we think aboutdiseases and weeds and insects
(20:56):
and you know I certainly hopeinto the future that we can find
a way to remote sense thoseproblems.
So often in today's agriculture, at least around here, we deal
with wet field conditions quitefrequently and I can remember
doing crop scouting and therewas no way I was ever going to
get that four-wheeler to goacross the field because it was
(21:18):
just a slop hole, it was so wet.
So if you can remote sensethose things with a drone, with
a plane, with a satellite, withsomething, so that you don't
actually have to physicallydrive across it first of all,
you're going to be saving thatagronomist a lot of time because
hopefully you get some remotesensing data back that you can
(21:39):
interpret and if you can have AIhelping you interpret that,
that can make those decisionsthat much faster.
But I know there's a lot of workthat has to be done before we
have those reliable models outthere, and I do think it is
important to remember inagriculture that we have so many
variables because it's anatural system.
(22:02):
Mother nature always wins, andone of my favorite sayings in
agriculture and life sciencesand then an enzyme happened.
We always have to remember that.
You know, we've got thesechemical equations that don't
balance because there's enzymes.
It's a natural system and sohopefully AI will help us shore
(22:22):
up some of these things that,quite frankly, algorithms
haven't been able to reliably do, and it's going to be fun to
see where it goes into thefuture.
I think and, travis, I thinkyou did get some extra training
in AI.
Just a quick question inagriculture where do you think
are some watchouts and thingsthat we should be careful with
AI as we're going forward and aswe're implementing AI into
(22:44):
precision agriculture?
Speaker 2 (22:46):
Yeah, that's a good question and I think, you know,
I think AI is going to belagging behind in agriculture,
you know, as compared to some ofthe other more profitable
things such as such as themedical or whatnot.
But yeah, and as I, as I kindof mentioned, there are a lot,
(23:06):
of, a lot of variables.
We're we're modeling an entireecosystem with farming, and to
do that and to make it so thatit's it's robust and intelligent
and works in every scenario ischallenging, I think, and I
(23:26):
don't know if it'll ever getthere.
Speaker 1 (23:28):
You know, we hope so and yeah, so, as we sit here and
we're having this conversation,when you think about your
career and the things thatyou've done and you look forward
to the future and thinkingabout where things could go,
what, what are some futurethings that that you might think
(23:49):
would be super fun to work on,or what do you think the future
of precision agriculture lookslike?
Speaker 2 (23:56):
boys that loaded question, I know, right, there's
no answer.
Speaker 3 (24:01):
You can't get it wrong.
Speaker 2 (24:02):
That's right.
Yes, I love remote sensing andI love the research behind it
and understanding how theprocesses work, and I think
that's you know.
As for me as a self, as adeveloper, that's what I hope.
As a developer, that's what Ihope.
(24:23):
As for precision agriculture ingeneral, I kind of alluded to
it before but, yeah, we havethese problems that are
preventing precision ag frombeing where it can be in the
future, and some of those arethe cost and the technology
behind it, as well as thesupport.
And that's what I love aboutthe gk technology is that we do
have the support, that that goesbehind that technology, and
(24:45):
that's super important to getpeople involved and to get
people to understand how, how itworks and where we're going
with that.
And so, in the future, for forprecision ag, that's what I want
to focus on is making kind ofthose easy buttons, just like an
SD drain, so that everydaypeople can use it and understand
(25:08):
the value of precision ag andhow we get there agronomy and a
background in farming.
Speaker 1 (25:19):
I just have to say that I think it's really
exciting to hear a developer,you know, with the ambitions of
making sure that we can gettechnology into the hands of
people and that cost shouldn'tbe a barrier and that it should
be simple enough for everybodyto use.
And hopefully, if Jodi and I doour job right, and everybody
(25:41):
else will make sure that we'rethere to support it, which is
pretty easy to do when you'vegot good products.
So anyway, that's very fun.
This has been a really funconversation and, again, I don't
think there's a lot of peopleout there that really know about
Travis Yike, because he's kindof a guy that is behind the
(26:05):
curtain, but he really is a lotof brain power that goes into a
lot of the products that we getthe opportunity to work with.
So thank you so much for beinghere with us, travis.
It was a very fun conversation.
Who knows, maybe we'll have tohave you back on again sometime.
Thanks for being here, thankyou.
Speaker 2 (26:23):
Travisvis, I would love it.
Thank you so much again.
Speaker 3 (26:26):
Thank you guys, all so much for listening this week.
And remember, with gktechnology we have a map and an
app for that.
And now it's time for a Geek Speak with GK
Technologies, Sarah and Jodyfriends and I can't wait to get
in the fields again.
Speaker 2 (00:17):
No, I can't wait to get in the fields again.
Speaker 3 (00:31):
Welcome back to the second part of our conversation
with the man behind the curtain,mr Travis Yike out of Wyoming.
We are so happy to have himback here on the podcast to talk
about and geek out about somany different things.
I'm not even going to list himhere because, as you'll see in
the episode, we're going to talkabout a lot of things, but it
(00:54):
is such a pleasure to haveTravis here.
Speaker 1 (00:57):
I think the last time where we left off we left off
with Travis had just gottenhired by Darren off with Travis,
had just gotten hired by Darren, didn't even have to turn in a
resume to get hired, which iswell shouldn't be surprising if
you listen to that whole episode.
He's done a lot of stuff withremote sensing, so let's talk
about this.
What exactly do you do at GKTechnology right now?
Speaker 2 (01:18):
That is a good question, and sometimes I ask
myself that every day, you andme both.
And sometimes I ask myself thatevery day.
Yeah, I've been involved inquite a few of the projects that
we have at GK, obviously theADMS software, which is our main
desktop software and it doesall of our precision ag and GIS
coding.
I'm also involved in our SDdrain and SD ditch and SD tile
(01:44):
and you would probably hear on aprevious podcast Paul Fuller
talked about some of our SDstuff and how we're doing there,
and so I went from one end ofthe spectrum working with valley
irrigation in places thatdidn't have enough water to
places that had too much water,and this is a totally foreign
concept to me from a guy fromWyoming that you know we were
(02:08):
struggling for, you know eightinches of rain a year.
Speaker 3 (02:12):
Let's remember that I had asked the question of why
Travis had focused on doingzoning for irrigated fields in
Wyoming and his answer was wegrow crops that are under
irrigation.
That's where they're grown isunder irrigation.
Speaker 1 (02:27):
So I agree, I come from.
Speaker 3 (02:28):
Western North Dakota and the concept of tile is just
like.
Why do we want to get rid ofthe water?
Speaker 1 (02:31):
But there are obvious reasons.
I can't imagine a farm thatdidn't own a scraper.
Speaker 3 (02:38):
But anyway.
So again, I mean you've done aton for SD drain.
I mean can you tell us about,like where, when you started on
working on SD Drain, where itwas, and then what came to, or
like where it is now and yourinvolvement with that?
Speaker 2 (02:52):
Yeah, I had to learn everything.
We started with Ditch, and so Ihad to learn everything about
why as you said, why we have toget the water off the field.
Yeah, and which was neat, yeah,which was neat.
The big part of that islearning GPS and doing RTK and
understanding all the codebehind GPS, which is a whole new
(03:14):
concept, and to understand andrelate that to how we implement
that with the actual hardwarewas a real step forward in how
precision agriculture is quiteuseful in that way is to get it
into hands, into the technologythat people can use it.
(03:37):
That way.
Speaker 3 (03:40):
At the end of the day , once we get maps into that
program.
That program is then tellingeither scrapers or tile plows
where exactly they need to be inthe field to get water to flow.
Can you talk more about whatyou have to know and how you get
a computer program to talk toan implement?
Speaker 2 (04:01):
Yeah, maybe I'll go back when I actually first
started school.
I went into civil engineeringand I did that, I think, for a
week, and I was like I don'twant to sit at a desk and do
math all day.
That is all I do now.
Sorry about that.
(04:23):
Sorry about that.
Yeah, so it's a lot oftrigonometry and that's one of
the math subjects I guess that Iwas good at and Kim's back and
haunts me to this day but isdoing that.
And we work with a greatcompany.
We work with Rust Sales andguys there.
They actually make the thatcontrols the hydraulics behind
(04:48):
everything, and I talked to thatDAC to tell it how to where to
send the implement or send thescraper blades up and down.
Speaker 1 (04:57):
So, travis, what is a DAC?
I mean, we talk about this allthe time with our customers that
use, you know, go to Rust salesand get their SD drain systems.
What exactly is that DAC thatgoes in the DAC, in the cab?
Speaker 2 (05:13):
Yeah, so DAC stands for digital to analog control,
and it's what changes from whatI send it as code in digital
form, from what I send it ascode in digital form, and it
changes that to the voltageswhich then converts it into how
(05:37):
much the hydraulics need toapply the pressure to make the
scraper blade go up and down.
Speaker 1 (05:39):
So essentially, it really is the place where you're
sending your computer code andthat's how we're ultimately
voltage first, but then it's thehydraulics.
Speaker 2 (05:46):
It's the little box that makes the magic work.
Yeah, there it is, that's right.
Speaker 1 (05:50):
It's the magic box, the man behind the curtain with
the magic box.
That's pretty neat.
We've had so much success as acompany with SD drain and SD
tile, and part of it is justbecause the system is programmed
so well and it works so welland the farmers love it.
The farmers love getting themaps in the background that work
(06:14):
so well with it as well.
In addition, we have a greatsystem for making surface
drainage maps and a great systemfor making drain tile maps.
Thank you, travis.
I just think it's important foreverybody to know that it
really is quite revolutionary,and Rust Sales does have
incredible customer support.
If you need help figuring outhow those systems work in the
(06:36):
tractor cab, they just haveincredible customer support.
We've been so lucky to partnerwith them.
Now, did you do most of theprogramming on SD drain and SD
tile, travis?
Speaker 2 (06:47):
Yes, yep, so some of the map controls we were able to
reuse those from ADMS, but thenthe rest of it is from scratch
that we put together and it'sbeen in.
How long have we had SD here?
Since 2014, I think.
Speaker 1 (07:04):
It was a long time before I came to work here.
Speaker 2 (07:06):
So it's been out there now for 10 years and I
think that's one of the thingsthat I was concerned, I guess,
or trying to get across withPrecision Ag is that I think
there's a few things inPrecision Ag that we all
struggle with, and that's thecost of it.
Sometimes it can be costly toimplement and especially if you
(07:27):
have like a small acreages oreven, you know, just getting the
cost down so that everydayusers can use it.
And then the second majorproblem is the technology of it.
Is that there needs to be, itneeds to be simplified so that
everyday people can use it in away that's In ADMS.
(07:48):
Don't get me wrong.
Adms is a wonderful product andit takes a lot of skill to use.
There's not an easy button atall in ADMS, but in SD a lot of
farmers they need just a two orthree button clicks to get to
everything that they need to doand I think that's important
with the future of precision agis to make that available to
(08:11):
people.
Speaker 3 (08:18):
I'll just say thank you so much for making it a
simple two, three button clickand SD drain and SD ditch.
Because, absolutely, as apractitioner of precision
agriculture, as a farmer, right,there are so many things that
you need to know as a farmerthat it is very difficult to sit
down and say, hey, I'm going tolearn how to do GIS and
understand you know how allthese maps fit together, and
then go out and do this.
It's just something that when,in the grand scheme of all the
(08:38):
decisions that have to be madeas a farmer, when it's like,
okay, I've got to take a coupleweeks off to learn how to this
completely new concept, it'sthat you're right, that cost,
it's a big barrier.
So the simplicity of it is huge, and that probably took a lot
of time on your end and thinkingabout how do I make this
process that's agile enough,that can be done, and two or
(09:01):
three button clicks.
So thank you for taking thetime to think about that while
you were developing and puttingtogether those systems.
Speaker 2 (09:10):
And it's probably more of the reason that or just
that it didn't come from adrainage background is that, you
know, I had to understand itand come back through it, so I
wasn't able to look at what wasout there already.
And it's and I think maybethat's another reason of the
success for it in trying to dumbit down is that I was
(09:31):
understanding how the drainageprocess works at the same time I
was developing it.
Speaker 3 (09:36):
That's really interesting, right?
Because a person would thinklike, oh, you'd have to really
know how these other programswork in order to make our own.
But your point is is thatbecause you you didn't have like
a quote-unquote bias of how thesystems already worked, so you
could, you know, make it yourown with the mind of simplicity
as or like, with the point ofsimplicity being your end goal?
Speaker 1 (09:59):
yeah, that's huge.
So question for you um, in theADMS software that we have, we
have the drainage window andwe've got watershed modeling and
we've got tile design.
Did you program those or didDarren program those?
Speaker 2 (10:16):
Darren, yes, we originally had a drainage and a
tile and Darren had done a lotof that in watershed modeling
and Darren had done a lot ofthat in watershed modeling and
when we moved over to the SDproduct I redid a lot of that
and we've had two different nowdrainage models in ADMS.
You know, that interesting kindof looking at the research
(10:38):
articles and putting researcharticles in the code was
difficult but it's fun at thesame time.
Speaker 3 (10:46):
This is kind of a weird question.
Do you have a favorite researcharticle that you reference a
lot or think is written verysimply?
Speaker 2 (10:55):
No, not a favorite.
There's so many researcharticles and they cover so many
topics, as does ADMS covers somany topics, and so it's hard to
pinpoint one down.
I guess, as I go into some ofthe new stuff that I'm learning
with artificial intelligence andwhatnot, that a lot of those
articles I could not find hardlya single article on using AI in
(11:19):
precision agriculture.
A lot of it is looking at,maybe like the USDA on saying
hey, this is soybeans and thisis corn, and doing a broad
spectrum of average and using AIfor that.
But as for more of theprecision ag stuff, I had to
scour.
I guess all the money for AI isgoing into technology and
(11:41):
entertainments and into themedical field.
Speaker 1 (11:44):
Interesting.
You're working on a number ofdifferent projects right now in
the background things that aregoing to be coming out into the
future for GK Technology.
Do you want to share with uswithout sharing too much?
What kind of fun new toys arewe going to get to play with
(12:05):
Travis?
Speaker 2 (12:06):
Well, so, as I was saying, part of Precision Ag and
the thing that I think about asa developer in the background
is understanding how to getcosts out lower, I guess, for
people and to make thetechnology widely available
(12:26):
available, and so I so right nowwe're coming out with, with a
phone app like as, where, um youknow, farmers can have
precision ag gis um in in theirpalm of their hand with their
phones and hopefully use it thatway, and in this case it'll
it'll be used a lot for forsurveying and for record keeping
and for sharing data back andforth between our main program,
ADMS, and I think that'll besuper helpful to our customers.
Speaker 1 (12:49):
And soil sampling.
Speaker 2 (12:51):
Absolutely soil sampling.
Speaker 1 (12:56):
We've got to have it in the soil sample rig.
It's going to be a lot of fun,and so if you're looking for
that way that you can seamlesslydata exchange between this app
and your ADMS software, it'sdefinitely something to check
out.
I think it's going to be a lotof fun.
What other fun projects are youworking on right now?
Speaker 2 (13:14):
Yeah, sd Drain is always expanding.
Right now we are testing ashaping product which will be
coming out, hopefully, eitherend of this year or next year,
after we do a bunch of testingon that.
Speaker 1 (13:31):
And that's land shaping right.
Speaker 2 (13:33):
That is land shaping, so taking an entire field and
getting rid of your potholes andmaking it so that water runs
downhill Yep.
Speaker 3 (13:43):
One of the questions I get when I go to my fiance's
home state is about terracing.
Has there been anyconsideration to land shaping to
do like terracing?
Speaker 2 (13:52):
Yeah, absolutely, and I think that's what our shaping
program will be used is forsome of these things like
terracing or creating.
You know if you're doing ricepaddies or we are just leveling
areas off.
Yeah, yeah.
Speaker 1 (14:07):
Because really the focus of SD drain and into
itself, when we think aboutdrainage, surface drainage is
just kind of to get rid of thatpothole that's out there, right,
and get the ditch clean.
But whereas with land shapingyou are, you are literally able
to create a dam.
If you need a dam in a place orget the water to flow, you can
(14:29):
literally shape the land.
Speaker 2 (14:32):
Or a golf course.
Speaker 1 (14:33):
Yeah, there we go.
I like that.
That's pretty fun, so that'sgoing to be something that's
going to be new.
Coming out is land shaping.
Speaker 2 (14:56):
You've got that app, so you're working on all kinds
of fun projects then Anythingelse use, whether it's copying a
politician's voice or creatinginappropriate images or whatever
it is, and there is some tabooaround it, but I think there is
a lot of potential in the futurethat we can use artificial
intelligence in precision ag tohelp us make better
(15:19):
decision-making processes.
Part of the problem withprecision ag, I guess, is that,
yeah, there are a lot ofvariables, right.
We have soil data and thequality and the types of soils,
and we have you know how thatrelates to fertility of the
plants and the soils themselves,whether it's nutrients or water
(15:41):
management, and then we gotenvironmental factors such as
rain or hail, or pests or weedseven, and so agriculture has a
lot of variables and and there'slike disciplines, right, that
like focus in on each one ofthese variables and to expect a
farmer or a consultant oranybody rather to to know any
(16:02):
and all of this stuff is you'vegot to have four or five
different guys in the field todo that, and then to wrap that
all up into a precisionagriculture software package
that people can utilize andunderstand is difficult to do as
well.
Intelligence may be a bridgebetween all of these different
(16:24):
variables, to understand andmake sense of some of these
variables that are hard to model.
Speaker 3 (16:32):
So GK sent you back to school, right?
Speaker 2 (16:36):
Actually, when I was in undergrad there AI is not a
new thing they're calledMarkovian decision models and
these were created back in likethe 1960s or so, and so AI has
been around for a long time.
Even when I was in undergradthere, they had computer
programs for remote sensing thatcould model and do
(16:58):
classification coverage using AI.
It's only within like the lastI don't know even five or eight
years that it's become popular,and I think some of the, the,
the coding Google has some codeout there that's open source,
that that the everyday publiccould use and and and to
(17:20):
understand and create their ownAI models that way, and and then
now we have ChatGPT and Geminiand all these other things.
That AI is kind of blown out ofproportion now, but so it's
been out there for a while andwe're just now starting to
understand and utilize it betterin our everyday practices as AI
(17:40):
develops more and people getmore interested in it.
Speaker 3 (17:44):
So what exactly is artificial intelligence, ai,
what exactly this is and whatdoes it mean when we say
something is AI?
Speaker 2 (17:52):
Yeah, that's a good question and it's hard to.
One of the things I learned inone of the internships I had
with the Department of Energywas to create a grandma speech
right.
This is the speech that youtell your grandma when she asks
you, hey, what do you do?
And you have to break it downinto terms that she can
(18:14):
understand it right.
Speaker 3 (18:15):
I love.
I'm sorry, I love.
It's like you learned about thegrandma speech working for the
DOE.
Speaker 1 (18:20):
I just like that a lot.
Speaker 3 (18:22):
I get a kick out of that, anyways.
Speaker 2 (18:25):
And so I guess here's the way I would, I would define
ai.
Let's take a giant plinko gameand you have a different maybe
you got a little round ball atthe top and you drop it down and
it goes through all thedifferent pegs, right and uh.
And then at the bottom maybe iswe have, uh, you, our bins at
the bottom, and these are kindof like our decision making
(18:47):
process, right.
And so as we make a decision,as we sit here as we talk or as
we think about a solution or aproblem to something, or problem
or solution to something I saidthat backwards that we have a
Plinko that goes through and itgoes through all of our
different nerves in our head andit bounces off of each of the
little pegs and finally it comesdown to the bins at the bottom
(19:08):
where we make a decision.
And so as we AI is I'm going tomake the analogy here that if we
change the maybe the diameterof each of those little pegs in
that Plinko game and we roll itdown, that it will we can make
it so that that little ball canroll in the right bin that we
(19:29):
want it to.
And AI is learning that.
Hey, if I change this way ofthis peg differently than this
peg, then we can make it rolldown into the right bin and we
have different inputs.
So we may not even have acircle, we may have a square or
an octagon or some geoid that weroll down the Plinko and after
(19:52):
a while we could have a milliondifferent little pegs that it
can, which is all the variables.
So some of those variables Italked about, such as soil or
water, nutrients or weeds, andif each one of those models was
talked about, such as soil orwater, nutrients or weeds, and
if each one of those models wasa different little pig that it
could roll differently based onhow that geoid was shaped, and
roll it out into the right bin,does that make sense?
Speaker 3 (20:15):
I think the way I'm understanding this is that
you're giving the right amountof weight to different
explanatory variables to accountfor more of the variability
that affects an outcome, andyou're accounting for more of it
.
I think AI is.
Speaker 1 (20:35):
I think your description made sense and I
think AI is going to have a fitin agriculture and I think AI is
going to have a fit inagriculture.
I know that there's a lot ofconversations around different
ideas that we can do with AI andI think some of the decisions
that are some of the placeswhere we may need help modeling.
You know we think aboutdiseases and weeds and insects
(20:56):
and you know I certainly hopeinto the future that we can find
a way to remote sense thoseproblems.
So often in today's agriculture, at least around here, we deal
with wet field conditions quitefrequently and I can remember
doing crop scouting and therewas no way I was ever going to
get that four-wheeler to goacross the field because it was
(21:18):
just a slop hole, it was so wet.
So if you can remote sensethose things with a drone, with
a plane, with a satellite, withsomething, so that you don't
actually have to physicallydrive across it first of all,
you're going to be saving thatagronomist a lot of time because
hopefully you get some remotesensing data back that you can
(21:39):
interpret and if you can have AIhelping you interpret that,
that can make those decisionsthat much faster.
But I know there's a lot of workthat has to be done before we
have those reliable models outthere, and I do think it is
important to remember inagriculture that we have so many
variables because it's anatural system.
(22:02):
Mother nature always wins, andone of my favorite sayings in
agriculture and life sciencesand then an enzyme happened.
We always have to remember that.
You know, we've got thesechemical equations that don't
balance because there's enzymes.
It's a natural system and sohopefully AI will help us shore
(22:22):
up some of these things that,quite frankly, algorithms
haven't been able to reliably do, and it's going to be fun to
see where it goes into thefuture.
I think and, travis, I thinkyou did get some extra training
in AI.
Just a quick question inagriculture where do you think
are some watchouts and thingsthat we should be careful with
AI as we're going forward and aswe're implementing AI into
(22:44):
precision agriculture?
Speaker 2 (22:46):
Yeah, that's a good question and I think, you know,
I think AI is going to belagging behind in agriculture,
you know, as compared to some ofthe other more profitable
things such as such as themedical or whatnot.
But yeah, and as I, as I kindof mentioned, there are a lot,
(23:06):
of, a lot of variables.
We're we're modeling an entireecosystem with farming, and to
do that and to make it so thatit's it's robust and intelligent
and works in every scenario ischallenging, I think, and I
(23:26):
don't know if it'll ever getthere.
Speaker 1 (23:28):
You know, we hope so and yeah, so, as we sit here and
we're having this conversation,when you think about your
career and the things thatyou've done and you look forward
to the future and thinkingabout where things could go,
what, what are some futurethings that that you might think
(23:49):
would be super fun to work on,or what do you think the future
of precision agriculture lookslike?
Speaker 2 (23:56):
boys that loaded question, I know, right, there's
no answer.
Speaker 3 (24:01):
You can't get it wrong.
Speaker 2 (24:02):
That's right.
Yes, I love remote sensing andI love the research behind it
and understanding how theprocesses work, and I think
that's you know.
As for me as a self, as adeveloper, that's what I hope.
As a developer, that's what Ihope.
(24:23):
As for precision agriculture ingeneral, I kind of alluded to
it before but, yeah, we havethese problems that are
preventing precision ag frombeing where it can be in the
future, and some of those arethe cost and the technology
behind it, as well as thesupport.
And that's what I love aboutthe gk technology is that we do
have the support, that that goesbehind that technology, and
(24:45):
that's super important to getpeople involved and to get
people to understand how, how itworks and where we're going
with that.
And so, in the future, for forprecision ag, that's what I want
to focus on is making kind ofthose easy buttons, just like an
SD drain, so that everydaypeople can use it and understand
(25:08):
the value of precision ag andhow we get there agronomy and a
background in farming.
Speaker 1 (25:19):
I just have to say that I think it's really
exciting to hear a developer,you know, with the ambitions of
making sure that we can gettechnology into the hands of
people and that cost shouldn'tbe a barrier and that it should
be simple enough for everybodyto use.
And hopefully, if Jodi and I doour job right, and everybody
(25:41):
else will make sure that we'rethere to support it, which is
pretty easy to do when you'vegot good products.
So anyway, that's very fun.
This has been a really funconversation and, again, I don't
think there's a lot of peopleout there that really know about
Travis Yike, because he's kindof a guy that is behind the
(26:05):
curtain, but he really is a lotof brain power that goes into a
lot of the products that we getthe opportunity to work with.
So thank you so much for beinghere with us, travis.
It was a very fun conversation.
Who knows, maybe we'll have tohave you back on again sometime.
Thanks for being here, thankyou.
Speaker 2 (26:23):
Travisvis, I would love it.
Thank you so much again.
Speaker 3 (26:26):
Thank you guys, all so much for listening this week.
And remember, with gktechnology we have a map and an
app for that.
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