Show Notes
What happens if you pair artificial intelligence with drones? Among other things you make life easier for tree growers, who can now count, measure, and more efficiently take care of their crop. Dr. Yiannis Ampatzidis and Matt Donovan are the developers of Agroview, a Florida startup invention and a 2020 Cade Prize finalist. They explain using basic drone images, Agroview’s AI and data fusion method provides very accurate information on thousands of acres in hours for what normally takes agricultural producers weeks.
TRANSCRIPT:
Intro: 0:01
Inventors and their inventions. Welcome to Radio Cade the podcast from the Cade Museum for Creativity and Invention in Gainesville, Florida. The museum is named after James Robert Cade, who invented Gatorade in 1965. My name is Richard Miles. We'll introduce you to inventors and the things that motivate them, we'll learn about their personal stories, how their inventions work and how their ideas get from the laboratory to the marketplace.
Richard Miles: 0:38
Spying on trees, what are they doing out there? It turns out if you pair a drone with artificial intelligence, you'll find out all their secrets. I'm your host, of Radio Cade. Today my guests are Dr. Yiannis Ampatzidis and Matt Donovan of Agroview, a 2020 Cade Prize finalist. Welcome to the show, gentlemen.
Matt Donovan: 0:54
Thank you, Richard.
Dr. Yiannis Ampatzidis: 0:56
Thank you for having us.
Richard Miles: 0:57
So first of all, I have to confess that I'm a sucker for any topic that has the word drone in it. My wife got me a little drone a few years ago and I have to become highly proficient and wasting lots of my time taking pictures pretty much of nothing, but they're pictures from a thousand feet. So it's cool. Right? I'm guessing you all have to be slightly more productive with your time and the technology. So why don't we start Yiannis, if you could describe for us what the core product of Agroview is, which as I understand it marries drones with artificial intelligence to take lots of pictures of trees. So why don't we just start out? Why trees? What are those pictures tell you? And more importantly, what does it tell the person growing trees who presumably is going to buy this product?
Dr. Yiannis Ampatzidis: 1:34
Yeah, that's a really good question. First of all, let me start from the beginning of Agroview, so there's some tools, Agroview is a cloud-based application. So actually it's like a software that analyze and visualize the images from drones, but also for ground based sensing system . So why do we developed Agroview for tree crops and vegetables is because we identify that there's same gap. There are not so mainly tools and solutions for specialty crops, like tree crops and vegetables, regarding on drones. And then the main idea here is to convert the data that we collect the information to some kind of practical information, useful information that the growers , the managers can use. There are samples for row crops like wheat, soy , bean , cotton , but very limited solutions that are available for specialty crops. That's why we developed Agroview. And again, the main goal is to convert data. For example, the images that we collect from drones to information, to something that we can really use.
Richard Miles: 2:41
So the real secret sauce here is the AI, right? Because obviously drones have been around a while. UAVs have been around and under development getting rapidly better since the nineties. And I've heard about all these potential applications, including agriculture. It never thought exactly how's that going to work? So Matt is this sort of the first time, or you are the first company to actually take the idea of using AI algorithms. You have these images, which we've been able to get for a long time. And actually as Yiannis said, do something practical with them.
Matt Donovan: 3:11
Well, I think as Yiannis mentioned in some of the more popular crop or more attended crops like corn or wheat, there've been utilizations of this, but in the specialty crop market, like citrus almonds, like specialty tree, fruit crops, not so much. And that lack of attention of providing AI tools is really the gap that Yiannis mentioned before. So while we're not the first to try it, I think in the specialty crop market, we're the first to really prove that what the Agroview platform does. Yiannis and his team have actually gone through the large scale commercial test. It's not a lab specific, it's not a controlled environment and they've published openly the results that Agroview achieves. And that's something that's novel and unique about the Agroview platform is that it's really gone through the scientific rigor that a lot of products will make claims that often they can't prove. So in that respect, we look at it as the first platform. That's proven the ability to take data from a drone, but also to take data from ground sensing systems and then have the AI sort of crunch everything together. And as Yiannis said to take multiple layers of data, but then produce a valuable piece of information, which the grower can then use to take action on and ultimately starts to get into the business impact that information then turns into actionable intelligence as it were. And hence our agriculture intelligence, the name of the company had come about is to have Agroview, create actionable intelligence that makes a business impact, but something else that's a grander vision of Yiannis is to start making impacts to the ecosystems that are around the growing environment and the environment in a longer view and in a more grand scale to create sustainability in those growing regions.
Richard Miles: 5:07
So one thing that impressed me when I watched the video of Agroview and the product that you have in the market is just merely knowing how many trees say citrus trees. For example, you have, it's a valuable piece of information to get, I guess, crop insurance for a number of different reasons. And to note where your gaps are, where you might have a row of trees aren't doing well, but Agroview does more than that, right? I mean, it doesn't just count trees and say, okay, you're missing four trees or three trees there. There, there are other things that you capture about the health of the crop itself or that how the plant is doing that, I guess, affects decisions on fertilizing or whatever. So Yiannis, how does that work? You mentioned ground sensors as well in order for this to work to its maximum capability, you're pairing a UAV with cameras and are you also then deploying an array of ground sensors so you can capture other data like how tall the crop is or how it is, is that how it works?
Dr. Yiannis Ampatzidis: 5:58
So with the dome , we can collect a lot of information, as you said, we can count crops, plants, which is very important and we can detect gaps, income gaps, and also develop a stress index. And we can also estimate plant nutrient concentration, which is very important for a precision fertilizer application.
Matt Donovan: 6:20
And that's using the UAV imagery, Richard.
Dr. Yiannis Ampatzidis: 6:23
Yeah, the UAV spectrum of data, which we really collect from my multi-spectral camera imaging. So doing that, you develop these maps, that they have different zones with different colors and these maps can be applicable or they can be used by precision and variable rate fertilizers. And that means there's a variability in the field. So you don't need to apply the same chemicals. The same inputs in general, it can be anything else can be water to the crops, but you applied based on the need. And this is where the savings comes. And this is how we can also try to reduce any negative environmental impacts . So we apply in this case, fertilizers as needed to the specific areas. We can even go down to the climate level. This is what we do with drone images, but on the same time we can analyze data collected from, for example, sprayers and fertilizers that we are developing new smart technologies, sprayers, and fertilizers that at the same time that they spray, they collect data that we convert back into information and example can be, we can also detect and count trees , but also assess the health that can be connected with the data collected from drones and all this information can be used also for yield prediction, which is a very important task for logistic purposes.
Matt Donovan: 7:47
So the drone imagery is an input into Agroview. The application map is an output from Agroview into the field for sprayers. But when the sprayers are spraying, we equip smart sprayers with additional data collection items that then become inputs that creates a richer and more detailed set of inputs for the agroview system to assess, which makes it much smarter. And the amount of data that we start to look at as inputs coming into Agroview that the artificial intelligence algorithm is dealing with starts to be massive. But that's the whole point. Precision agriculture is making that impact of taking those individual units of data, whether they come from a drone or they come from collecting from the sprayer, which is a nice dual use, right? It's an output from Agroview, but we also utilize it as a smart opportunity for us to collect more information, to then provide additional details for the AI to assess. And it creates a richer set of information moving forward, and it builds and builds and builds. It goes from 2D in the air to 3D on the ground. And the collection of that data over time gives us a 40 view over the course of time that really sets Agroview apart.
Richard Miles: 8:58
So that's really kind of the beauty of AI, right? It's not like you have a bunch of smart coders. They write a great program and then it has to be constantly updated by smart coders. The AI kind of gets smarter on its own just because you're getting this massive inputs of different types of data. And you're combining your interests solution.
Matt Donovan: 9:13
Terrific point. It's almost a fully automated platform in that sense.
Richard Miles: 9:18
Several months ago, I talked to the president of the National 4H Council and she was telling me the history of agricultural technology goes way back to really Abraham Lincoln, who founded that land grant college system. And as a requirement, it made the sharing of agricultural technology widespread. And one of the great results of that is that farmers have generally always been early adopters of technology because they recognize the value right away because it affects their costs. It affects their ability to successfully harvest crops. And so on, Matt maybe you can take this, what sort of reaction have you gotten from? I'm sorry, I just got to use the pun from farmers outstanding in their field. Are they reacting to this like, Oh, this is great or do they still have questions or a little bit of skepticism or their cost issues involved is just an intense capital investment Say in Agroview or similar technologies, or what kind of feedback are you getting from them?
Matt Donovan: 10:11
Well, the farmers are certainly looking for the proof they are adopters, but as a customer persona, if you will, they're very much proof in hand. And certainly be honest, works directly with a lot of growers who have seen the Agroview system. And it can give you some feedback. I think from a market perspective, they're looking for proof. They will adopt the Agroview system itself is in keeping with a lot of the way that their products are priced on a per acre basis. So we've adopted kind of the norm of what they follow with pricing to try to show them that value. So far, there's a little bit of wanting to calibrate what Agroview is able to produce using UAV imagery or ground collected data with what they already know. The beauty of the system, actually in that large scale, scientifically proven test was a commercial plot and it was ground truth by Yiannis and the team, the published paper that was done took into account the ground reality often referred to as ground truthing methods to compare it to what the UAV collected images were. So what we're finding is if the growers give us the chance, we can show them that the data that's collected via the drone alone is very comparable to the information they see on the ground and in the palm of their hand, as it were lots of work to go, but that's what we've seen so far. And the good news is the algorithm is very accurate with regards to that. So I think what they they're seeing out of the Agroview system pairs up nicely with this sort of healthy skepticism of should I adopt and get these promised c osts savings. And the reality is, is very positive results, but also with a pinch of making sure that they are putting money into an advanced technology, that's going to be as good as what they can see and feel on the ground. They're very intuitive. The data element is actually something that I think really is an added element for them b ecause growers are extraordinarily intuitive about what's going on in their fields. But that data element I believe is, is the gap that we're really filling in the market.
Richard Miles: 12:23
So that's a really good point, Matt, and give me a feel for what in best case scenario, if a grower adopts the technology uses it correctly, there are no malfunctions, what are the potential cost savings to them? And I guess as a corollary of that question, what's the next best alternative, because as you said, growers have highly intuitive sense of how their crops are doing, what would prevent a skeptical grower from saying like, look, this looks really cool and snazzy, but you know, honestly I can get my truck and in an hour drive around my fields and get the same info. What are the magnitude of cost savings? Obviously that would take a lot of time driving around and doing it in person. What is your value proposition in a best case scenario,
Matt Donovan: 13:01
Let me break it into sort of three components. One is , is that these tree counts are critically important for a lot of decisions that they will make. But tree count is also a regulatory requirement in order for a grower like a citrus grove, for example, to get crop insurance through the USDA, they have to do an inventory. And so right now the current method of trying to count trees is a couple of dudes jump in a truck, an old dusty truck, probably with no air conditioning and a couple of clickers like handheld clickers. And they drive up and down each of the rows, clicking on the right, clicking on the left. Now, as far as that process or method is used, it's extraordinarily error prone, a hot summer in Florida to try to keep your concentration in a hot humid orange grove in Florida in the middle of the summer is not an easy task. Um , and it's also very carbon heavy, which gets into the environmental impact. But from a practical perspective, a thousand acres of survey manually costs $15,000 and takes four to six weeks from the Agroview perspective we're in and out of that same thousand acres in two or three days, no truck touches any of the inner parts of the grove. So it's carbon neutral and the information is so much more accurate. So just on the tree count alone, we have proven 99 plus percent accuracy. So just on the practical side of getting insurance and account, that piece of it is there. Of course, the health statistics, the height of the tree, the canopy, the stress, and the overall health of the tree goes towards a much richer mosaic of information for the grower there. And then the decision between the tree count and the health qualifications, if you will starts to factor in what they're considering potential yield, but the tree count and its accuracy becomes so important to any formula that they're using. It's a highly weighted variable. I mean, plug in the wrong tree count and into whatever estimating formula that they're using, whatever method that they might be using tree count can throw off what they may think is coming at harvest by a lot, one degree off now means way off in the future. The nutrient analysis, probably the biggest impact. And that's something that on a qualified costs, the Agroview system is going to just absolutely make something that's 90%, less than cost . I mean, it's massive savings. And the methodology for us to do nutrient analysis is so comprehensive because it accounts for the whole field, which right now they utilize a very expensive lengthy time process to collect leaf samples, send it off to the laboratory. Again, us flying for a thousand acres in two days is what takes weeks and weeks in tens of thousands of dollars just to render the information that the Agroview system can produce within 48 to 72 hours.
Richard Miles: 15:58
Wow. That's quite impressive. Yiannis, are there any technical limitations in terms of other types of applications that this could be used for? Like for instance, right now you're going after specialty crops like citrus trees, for instance, could this be used for cattle? For instance, I had a guest on a couple of weeks ago talking about the next generation of beyond visual line of sight UAV that can travel much farther distances and could a Texas cattle rancher who has a gazillion acres and thousands of heads of cattle could eventually this sort of technology be used for them to keep track of the cattle and the health of the cattle and so on, or is this really limited to stationary crop ?
Dr. Yiannis Ampatzidis: 16:37
Yeah, that's a really good question before I answer this and let me emphasize a little bit with tree count. And I just want to make clear here that this is very important especially for Florida because of citrus greening growers got to remove a lot of the trees. That's why they don't really know how many trees they have in specific blocks before it was easier that you put it that way. You know that you have maybe 10 acres, you planted 160, let's say the record. So you can estimate. But now with the greening, citrus greening, you might have 50% of them may be gaps. So there'll be trees that they h ad to remote, right? So this is also another potential. You need to know how many gaps you have. You need to know if you want to r eplant, so how many trees you need to go order from a nursery. So that's why tree detection is our first task, different AI models. I t's not just a simple AI. I usually say that has different levels of intelligence. So going back to your question, y ou a re totally right. What we try to do with, w ith other crops like tomatoes, s quash, watermelon, w e even try to detect diseases. At the early stage, early disease development stage, which is the most critical. So to detect t he disease with no visual symptoms on very small symptoms, this is the critical step. I know a lot of growers spray proactively just to be sure that there will be no infection, but sometimes a re infections. T here a re diseases. So if you detect that, t he early stages can save a lot of money. You can control, you take the best management tactics, and then you can control the disease. Before that spreads throughout the field that can save you a lot of money. We've seen examples t hat a d isease can totally d estroyed the entire crop. So now about the cattle, we can do something similar, like how we develop AI based models to detect diseases in crops. We can do something similar with lifestock, using drones, using g round-based sensing systems. We can, first of all, identify individual animal and then collect some information. And actually we have a different project that we develop wearable devices, smart devices, to collect information from individual animal. It can be a horse, it can be cattle. So connected that with, as you said, d rone i maging, it can really help and you can develop a fully automated system. Again, like Agroview that analyze o f the data because the beauty actually comes from there. We can collect huge amount of data, but what you really do, the data is important part, r ight? In this case, if you have r eminds o f like hundreds of thousands of images, this is the big data issue, right? That's why you need big data analytics. That's why you need AI. It's very difficult for t he human brain to understand and analyze big data. But using AI, you can simplify and automate this process and you can have the critical information at the end, let's say t hat t his i s detection o r something like that in almost in real time or in mer real time. And this is the goal right now. This is where w e're going. W e a re not going to stay only for, let's say crops, but we're developing similar technologies for livestock in general.
Richard Miles: 20:04
That's really fascinating. I mean, as you said, the problem no longer really is the ability to collect data. We have all sorts of ways we can collect data. It's what do you do with the data and the masses of data that you're going to get and turn that into something very useful. I'm glad to hear that you are looking at livestock, just one story of the world we live in. Now we have a goofy little cat who just would disappear all the time. So we finally got him a pet tracker, right way too big for him it is made for a dog. It looks kind of ridiculous, but it turns out when we went live with this, the first time we got it, it was hilarious. Cause our son was in the Navy out in Guam and our daughter was in Hawaii, working in a hotel out there. And the night it went live, we all were watching from around the world. What's this cat going to do was going about 11 or 12 miles a day. I mean, just all over place. And we could see where he was in the neighborhood. And so I'm sure you're going to go after more than just the cat market. Cattle is much more lucrative than house cats, but you know, I had to step back and go. This is amazing that people scattered around the world can all look at where this little house cat is going. And imagine now what you can do with information wearables, for livestock and collecting obviously much more than just your location, all sorts of metrics on their health and so on.
Matt Donovan: 21:12
Well, it really points to the name Agroview really comes from all of the precision agriculture you need in one view. So like you and your family watching your cat would be akin to whether it's a grower or a livestock operation, to be able to see that information in one view, that is what the Agroview system is. As Yiannis said, trying to crunch through all that data and then present it in the case of most of this, which is kind of a map driven view, a map driven interface that you can get those stats 11 miles a day, that your cat was going. Probably might've been accompanied by a little map if it had it, if all of its little travels. So again, it's simplifying massive data into a very understandable view that can be seen by not just you and your four family members, but it could be multiple team members of the farm operation. All of them can have access to it the same way that you don't have to be in the same place, but that data is provided in one view, the Agroview as it were.
Richard Miles: 22:14
So one of the things we find really interesting on Radio Cade is I always like to kind of know a little bit about the background of the inventors and entrepreneurs that we talk to because they've all have very interesting paths to the invention or the business. So Yiannis, let's start with you. You're currently an assistant professor at the University of Florida Institute of Food and Agricultural. Sciences, otherwise known as IFAS, but you're originally from Greece and you move to the United States about 10 years ago. You know , I'm just curious, what were your first impressions of the United States and just want to turn around and go home. And then after that, how did you make your way to studying agriculture?
Dr. Yiannis Ampatzidis: 22:45
Okay. Moving to the US in 2010, I was at the Washington State University. So I had an opportunity to join a team, a really good team, as a postdoc research associate. And I think beginning and need some time to readjust that it was a totally different lifestyle, but I love it. And I liked the team and we work also developing precision ag technologies and they like the culture here and the connection between the universities and the industry where you really enjoy to develop technology . So applied research and develop new technologies that someone in really use. So after that, I moved to California, was the assistant and associate professor at the Engineering Department at the Cal State system. In 2017, I moved at the University of Florida at them Agriculture and Biological Engineering Department as an assistant professor. And then here in all these three States, I work with specialty crops. So tree crops and vegetables. Yeah . I really love my job. I think we have a lot of opportunities to develop new smart technologies and especially utilizing AI. So overall I'm super happy here. I enjoy my job and I love it. So no complaints at all.
Richard Miles: 24:04
And Yiannis did this run in the family where your parents involved in agriculture at all in any capacity?
Dr. Yiannis Ampatzidis: 24:10
Yeah, my grandparents, for example, they were farmers. My father was not a farmer, but he also likes to grow grapes, make wine. So I grew up in a small family . I always liked also engineering. Let's say I like to build stuff and this two came together. So that's why ag engineering.
Richard Miles: 24:31
So it sounds like from an early age, you kind of had a fascination with the idea of growing things and studying that, or was there a particular moment that you remember in school that you're like, wow, this is really cool. I want to know more about this.
Dr. Yiannis Ampatzidis: 24:42
I would say that it was mostly building or developing things. I remember even when I was like five, seven, ten, any project that I had to build something, it was like really something that I enjoy . So starting from there, then I like mathematics programming. That makes it very easy for me to follow this path. And of course, as I grew up, I knew about agriculture. It's very important. We need food, we cannot live without food. So.
Richard Miles: 25:10
We can't live without wine ether Yiannis,
Dr. Yiannis Ampatzidis: 25:13
Thats true, especially the Greeks.
Richard Miles: 25:15
So Matt let's turn to you. You come from a different background. You're currently the CEO of Agricultural Intelligence, which is a company that is taking Agroview to market. And you come mostly from a business background, but tell us about your path. Where were you born and raised and how did you get into the business arena?
Matt Donovan: 25:29
Well, I'm a native Floridian. I was born in South Florida. I was raised in the West Palm beach area. I lived there for the majority of my young life and after I got married and had a job opportunity, I moved to Gainesville, Florida where I reside today. I had grown up in a small business. My father ran a small business. So as much as growing wine or grapes and attending to crops, might've come somewhat through Yiannis background, mine was more of a growing up in a family that ran a business. I went to the standard things. I graduated college, started working in the corporate world and got married and found a place to live here in Gainesville, Florida. So I'm a native Floridian and got involved in various corporate work. And after a decent career doing that, I , I started my own management consulting company. And after I was doing a management consulting engagement, I came up with an idea for a piece of software. And so I wrote the piece of software myself, and it became a part of the telecommunications area. And I ran that company for 15 years and I am now lacking the coding skills required, but thankfully folks like Yiannis are much more talented in those areas. So that's my side of bringing some healthy background as an entrepreneur and the corporate work that I've got to try to lead the business side of Agriculture Intelligence and bring Agroview to market.
Richard Miles: 26:52
It sounds like a great partnership that you have going and perfect segue to talk about where you are now as a company, you've made a lot of progress. It seems like in the last year, in addition to becoming a Cade Prize finalist, you were one of the outstanding entries that we had this year. You've gotten a number of other awards and recognitions. Where are you as a company right now? And what are your next steps? So for instance, how many employees do you have and are you raising money or give us a snapshot of where you are in the life cycle of Agricultural Intelligence and Agroview as a product.
Matt Donovan: 27:20
Yeah. As a product were kind of that pre-revenue just starting to accumulate some sales. As I mentioned before, the growers are still vetting and calibrating the technology and trying to adopt that we're competing for several larger contracts, which will be good for growth. The natural revenue growth, we are seeking funding still officially. There's a small team of four that are mostly oriented around moving the product forward and sales. So it's a relatively small team, but we're looking to rapidly grow over the next year. So any healthy investors that want to do a proven product, we're out here to have a conversation with.
Richard Miles: 27:57
Well, I can tell you one story you probably will enjoy. It was about probably a little over 10 years ago, a company similar to yours, they're in the software space, but in healthcare for employees in the same building you're in right now, Matt in the innovation hub, they've done very, very well. And they're getting ready to have a very successful exit to very, very soon. So I've seen it happen. It can be done for sure. Along those lines. I'd like to ask both of you, you've got enough experience under your belts now in taking this idea, as far as you have, you're not done yet. You're still in the middle of the journey, but it's the legions of other researchers and entrepreneurs out there. What sort of advice would you dispense at this point to them? Like for instance, are there any mistakes that you've made that you think, you know, I wish somebody had told me about this, or why didn't somebody warn me about this particular obstacle that I might encounter? So Yiannis, why don't we start with you? Any regrets or any wisdom or advice you would dispense to maybe someone about a decade behind you wanting to do the same thing?
Dr. Yiannis Ampatzidis: 28:53
Sure . I had another startup at Washington State University. We had a really good idea and actually the growers tried to motivate us, to commercialize the technology that we developed and offer it as a service to the grower. There's something similar happened with Agroview the mistake was that we thought two of us actually, that we can also run the company. We have our, day jobs that as a professor or researchers. And we thought that, okay, maybe at the same time we can build and run the company, it was a huge mistake. We didn't have the time. Sometimes we didn't even have the time to answer the calls or emails. This time I was like, no, I'm not going to make this mistake. I need to find a great guy who ran a really good company and good CEO. And I was very lucky to meet with Matt. So I think, yeah, that was one of the mistakes. I will never forget. We cannot do everything. So we need to identify what our skills, what our capabilities and then partner with others,
Richard Miles: 29:50
It's a valuable mistake and a valuable lesson to learn. And it's actually occurs more often than you would think. Researchers thinking like, well, how hard can it be to take this idea to market? Cause it's a great idea. And almost invariable . It is a great idea, but that getting it to market and getting it capitalized and so on is, is tough road. And uh , a lot of people don't make it. Matt, how about you? You're in the business world by definition to sort of they're winners or losers or ups and downs. Tell us a little bit about what lessons you learned.
Matt Donovan: 30:17
I think the list of mistakes that I've made is so much greater than, than that. I would just actually focus on something. When I was in the corporate world, I was lucky to have someone who mentored me and of the various lessons as sort of a younger business person, was something that my mentor said was contribute every day. Find a way to make a contribution sometimes it's to yourself. But if you're contributing, you're often making something actionable. That's tied to someone else's goals. And often you don't realize it when you're younger, contributing to other's goals are actually the most important thing you can do to achieving the overall goals and ultimately any organization, any products, every company is comprised of people, the actions they take. And those two things are normally something that every single day you need to contribute to. So I sort of took that on as a life lesson that I believe helped me maybe avoid more mistakes than I would have made otherwise. And occasionally I look for those nice days where making that contribution every single day and the discipline of trying to contribute to every day kind of adds up over time. And the old saying is it's a marathon, not a sprint. And making a contribution is, are literally each step you take in that marathon. So make a contribution every day, some way, find a way to make a contribution and keep going. That's the essence of it.
Richard Miles: 31:57
That's great advice. Yiannis and Matt, you guys are doing great. I want to congratulate you again for the success you've had so far. You do have a great idea. I do think that you will succeed because I think you've done a lot of thinking about this and where the need is and how this is going to be used. So I look forward to having you back on your show after you've had your half billion dollar exit or whatever, whatever that can be. How about when you do your IPO, right? We'll have you back on the show and you can tell us some more lessons, but I want to thank you both for your time and wish you the best .
Matt Donovan: 32:24
Thank you. Richard.
Dr. Yiannis Ampatzidis: 32:25
Thank you Richard.
Outro: 32:28
Radio Cade is produced by the Cade Museum for Creativity and Invention located in Gainesville Florida. Richard Miles is the podcast host and Ellie Thom coordinates inventor interviews, podcasts are recorded at Heartwood Soundstage, and edited and mixed by Bob McPeak . The Radio Cade theme song was produced and performed by Tracy Collins and features violinist Jacob Lawson.
