 And welcome to Likeable Science here on Think Tech Hawaii. I'm your host Ethan Allen. Thanks for joining us here on Friday afternoon. With me today in the Think Tech studios, I have two staff members from SmartYields. We have Lizzie Schiller and Kristen Jamison. And they are respectively the education and growth person and the farm community lead, I guess. Right? Yeah. More or less. Right. So, welcome. Welcome. Thanks for being here. Thank you for having us. So, I suspect a lot of the people who are viewing this don't really know what SmartYields is because it's sort of a start-up, right? Can you maybe tell us just a sentence or two overview and then... Sure. Yeah. I mean, the easiest way to think about it is we like to explain to SmartYields as the Fitbit for farming, the Fitbit for agriculture. And so, you know, what that means, just like you have a Fitbit, you know, I actually have a Fitbit on my watch right now, like I'm running around and it's tracking all the data points for me so that I can see how I'm performing over time. Same thing for farmers. We can put sensors in the field and then that data from the sensors being traveled up to our app and our app is communicating to the farmers how their crops are performing 24-7. So, real-time monitoring and... I mean, Kristen, I think, can share a bit more, too. Yeah. We're bringing data science to the agricultural sector. Oh, yeah. So, okay. Right. Farming has often been rather slow to pick up on new tech and this is a real sort of a jump. I mean, real game changer, right? Yeah. So, this technology already exists for large-scale farmers but the majority of the world's farmers are small to medium-sized farmers and this technology currently isn't available to them because of cost limitations and other limitations. And so, by really tailoring our solutions towards small and medium-sized farmers, it's revolutionary to be able to give them access to this kind of data, to be able to make good decisions and take the guesswork out of growing. Excellent. Excellent. And so, how did this get started? It's a local company, right? Yeah. We're a local company based here in Honolulu just right across the street and we just went through Blue Startups, the Startup Accelerator and before that we... So, the team got incorporated a year ago and before that... Yeah, so, this is kind of the brainchild of Vincent Kimura. He came up with the idea about five years ago and since then has been searching for a team and, you know, he's been able to gather the necessary parts and, you know, build a really inspiring team to make this happen and we're able to go through Blue Startups and now we're going through Accelerate UH as well. So, yeah, yeah. So, we've got a little, I guess, video that will show us what... So, you know, the basic idea for the why is, you know, why are we doing this? Well, by the year 2050, our world population is going to increase by over 40%. And so, you know, how do we ensure food security? Exactly. Exactly. So, you know, just like Christian was saying, really large-scale farms have, you know, access to money and funds to have these technologies like satellite data imaging. That's a pretty large area. Pretty large area, you know, small and medium-sized farmers, they're not going to really get a lot of benefit out of that type of... We also have UAP data imaging. Of course, that's also very costly. You've got some on-the-ground sensors. Yeah, so this is really precision agriculture and using these terrible rate technologies. Once again, not necessarily as applicable for your small and medium-sized farmers. And right here, this can work for anyone. It can work from someone who has just a garden in their backyard to a school setting to, you know, small and medium-sized farmers like we've been talking about. Yeah, so there's real potential there then for this to grow on a global scale too and to get adopted more and more widely just as in Africa they sort of skipped the whole landline and went from no phones to cell phones, right? Exactly. There's been a lot of interest in Hong Kong, South Korea, Vietnam. So this is really a technology that's going global. You know, data science is the next agricultural revolution. Right. And the idea also, tagging on to Kristen with the data science, the idea is as we continue to pull and aggregate the data coming from all these farmers around the world, we can continue to learn and the data analysts can then inform and drive decision-making in the future. That's true because if you're at basically or some computer hooked to it is tracking all the data from the farmers, it can also alert other farmers say down the road that, oh, by the way, these guys over here are beginning to experience some sort of a back way. An investigation or whatever their soil is drying out now and blah, blah, blah. Right. Or maybe even something like, oh, I want to grow sunflowers in my backyard. Let me open up the app and see if there are any other people within my area that have grown sunflowers and what their recipe looks like, what they were doing. Yeah. And on the subject of data aggregation too, there are hosts of data that are stored away and siloed away from each other. But if we can aggregate that data onto a central platform, we can learn a lot more from it. Yeah. This is an amazing thing. SmartYields is a really interesting example of it. But the same thing is happening all around the world, basically. I have a colleague of mine who is an anesthesiologist at the Fetcher's Hospital in Seattle. And he started looking at several different data sets to try to figure out why so many of the Iraq and Afghanistan vets were committing suicide because it's a terrible problem. More people died from suicide than died from fighting. And what he found was that by digging into multiple sort of disconnected siloed data sets and pulling that stuff together, patterns emerged about these people's hospital visits, run-ins with law, social worker interactions, these kind of things all began telling a story and there was a predictable pattern basically, which of course now they can use to, when they see this pattern show up for living service members, that they understand these people probably in some sense at high risk. And so again, it's sort of the same kind of thing. It's taking multiple disparate streams of data, combining them and getting really rich information out. Yeah, really uncovering those patterns of what makes successful agriculture. Yeah. And because you can adjust it in multiple ways. There's something, 80 different kinds of sensors that are potentially available. So in some places you're not going to care so much about one thing or another. If your soil is well buffered, you may not care so much about pH, right? Right. And going off of the sensor data points, smart yields is hardware agnostic, meaning we are not specializing in working only with one specific type of sensor another. If a farmer has a type of sensor that they're already using on their property perhaps or they want to use, we are completely willing and interested in working with them to have our platform be able to pull that data so that they can use it. As long as they can sort of stream it out somehow, right? Yes, yes. Yeah, in some reasonable form. Yeah. You guys will take it and use it. That's wonderful. Right. Yeah, that's a little different from, again, another vaguely parallel thing that I think is global learning and observation to benefit the environment of a brainchild of Al Gore as many years ago where they've set up mainly at schools but a lot of places around the world, weather stations basically. But they got very clear from the start. It's like everyone's got to use the same stuff here so we know our measurements are all comparable, right? So there's one kind of rain gauge to use and one kind of wind, you know, this kind of stuff. So it's interesting that you purposely built, as you say, an agnostic platform basically so you don't care how you're getting the data just as long as it's coming in. Again, particularly for a global kind of thing, that's great because people can adopt and adapt what they've got, what they've got already going. Yeah. The hope is to make it as scalable as possible. Yeah. And this is really, it's intriguing the work I do is a lot of my work is around water and drinking water issues on remote Pacific islands and a number of them have very serious soil problems, particularly the lower islands, because they get washed over by the ocean periodically and they get too much salt in there. And again, being able to track salt levels locally from spot to spot can be an incredibly valuable thing. How long did the way you stay over your land here basically is going to determine how much salt got in there and that can give you some sense about the recovery time your soil is going to need. Timely alerts are extremely important for farmers in being able to mitigate and stop crop loss. And so having this real-time monitoring of data is imperative to being able to pursue overt serious crises on the farm. Right. Yeah, I mean it's sort of comparable to trying to play a football game. If you're blindfolded, right? You couldn't do it very well, right? The game would be sort of a joke if only every, you know, three minutes or so you were allowed a moment of sight and could see what went on. You couldn't really play the game very well, but in the same kind of thing, by allowing and encouraging continual real-time monitoring, you're really boosting the fine-grain nature of the farmer's understanding of what the situation is, right? And off that analogy of the game too, it's really hard if all of a sudden you take off your blindfold and the rules changed. And that's what we're finding all over the world is the rules for agriculture are changing because our climate is changing. And so, you know, what's been generationally appropriate in the past may not hold true in the future. And so we really have to learn what's worked well to be able to adapt to new farming as our climate adapts. Absolutely. Absolutely. Because things like, yes, soil temperature will be changing and that's going to change planting dates and, yes, maybe the suitability is in crops. Cold tolerant crops may be moved to further higher elevations, more northward latitudes. And especially when it comes to irrigation to, you know, what places might get wetter, dry places might get drier. And so as we have differing, you know, water rainfall patterns, that's really going to change the landscape of agriculture as well as pest movement, disease movement. Absolutely. The challenges facing farmers in the coming years are huge and we really hope that data science can be a solution. You sort of got to believe it. I mean, again, the challenge our film brought up with the population continuing to grow for the next several decades and the amount of arable land shrinking, we have something of a bad situation, shall we say, looming. And, yeah, you need all the help you can get and a good, having real-time data like that could be immensely valuable. So, I'm being told I think that we need to go on to break here at this point. So we'll come back after a brief break. I'm your host, Ethan Allen, here on Likeable Science with me today are folks from Smart Yields. We'll be talking more about it when we come back. Aloha. My name is Justine Espiritu and I am the co-host of Hawaii Pharmacy. This is my co-host, Matthew Johnson. And we are live with you every Thursday at 4 p.m. at thinktechhawaii.com. And our show focuses on Hawaii's local food community. We feature not only the farmers that are producing our food, but we also feature the supporters and other folks involved in the community that are trying to promote local agriculture. I'm Jay Fidel and I'm the host of Research in Minoa, Mondays from 12 to 1, on thinktechhawaii.com. Take a look at us and learn about geophysics, learn about planetology, learn about the ocean and earth sciences at UH Minoa. You'll really enjoy it. So come around. We'll see you then. And you're back here on Think Tech Hawaii, here on Likeable Science. I'm your host, Ethan Allen. With me today are Lizzie Schiller and Kristen Jamison from Smart Yields. We've been talking about this sort of amazing platform, I should call it, or application that combines multiple streams of data, depending on what's available, actually. A lot of very localized, but also some more high-level data and allows farmers to monitor their... the whole environment and many, many, many aspects of the environment on a more or less continuous basis. And a tremendously powerful potential to really enable the sort of optimization of growth, right? But the other thing that I was hearing is that you're also going... pushing this a little bit into the schools, right? And some of the schools have learning gardens, right? So this is the idea that schools have... do these gardens and use them to teach kids science and cooperative work and all that kind of... those kind of good things. And now, with this kind of tool, suddenly this really opens up new vistas here for education, right? Yes, exactly. So we've already actually partnered with a few schools, including Ilani. They already have our sensors and they're using our platform. And the idea is just like what you said, to really empower the students and the teachers to utilize this data science piece of agriculture, of gardening, so that the students are inspired and excited to hopefully be our world's future agriculture scientists and agriculture tech leaders. Yeah, it goes beyond that, though. So many students do not deal the data in a real way, you know, in a sensible way. And this connects data in it by doing it in a school garden and you understand what the value of regular, repeated observation is, what the value of keeping a log journal is, why, you know, how this data begins to build over time and give you this real sense, oh, you can see the cycle appearing in the data, it's wet, then it's dry, then it's wet, then it's dry, and you begin to be able to say, oh, I know we're going to need to go up and get more water on this thing because... Yes, and just like you were saying, the logging piece, that's also in our app as well. Not only are you seeing data coming in, but the students or the farmers have the opportunity to record their observations, take pictures, monitor how much water you're putting into your crops every day, measure, you know, the amount of growth and everything, so it's essentially data, analytics, an aggregator, and record, observation, note-keeper. Yeah, I don't know, it's very powerful and the trend in the I know in the islands here is place-based education and what this potential is is a very deep knowledge of place and really helps tie seemingly this almost, what do you want to say, very traditional sort of, this is my leon, this is the earth that we live on with just really cutting-edge technology, where you really find out, okay, this is the earth, but it's not quite the same as it was yesterday, and it's not going to be quite the same as it is tomorrow, right? Things are shifting continually in various ways and hopefully not too radically, hopefully bouncing back and forth within some limits, but again, great to know if you don't, if you start to exceed those limits or push towards some boundaries best you know about it early, right? And this can really enable a farmer or a teacher or a community to know much more about their their local environment and what's liable to happen. Exactly, exactly. And on the education side of things, just to add a little bit more, the pretty new standards, the next generation science standards, they're being adopted nationwide and as we're going through the standards and really looking at the alignment with smart yields and the NGSS, there's a lot of opportunities here. So we're in the process right now of developing curricula associated aligned with both smart yields and the NGSS, so we're very excited for the future with that too and partnering with more schools in the process. Yeah, absolutely. And it goes to even informal science education too, communities, a lot of communities these days are facing sort of increasingly marginalized environmental conditions and having this kind of data could really help them make a sensible choice about when is it that you sort of give up on your current place and say it's time to move our whole village and move it to higher ground or move it further south or whatever needs to happen versus just sort of saying maybe it's going to get better, maybe it's going to get better tomorrow and that being caught in a bad disaster. So very critical on a lot of different levels. I can see the value of this. What size is market yield? Where are these things being deployed? You say in schools locally and in farmers locally here. Yes, so we are a team of about 12 right now and growing in terms of the farms and the locations that we're working with right now. We're working with farmers in California, Washington, Oregon, of course, Hawaii, Hong Kong, Colorado, Colorado. And I think we've had over 80,000 on requests for demos. So there's a huge level of interest from the farmer community. I believe a lot of places, I believe places like Japan would want this where you've got real limitations on land and really want to maximize your productivity. Even I suspect places like China where they've messed up so much of their land that they are facing some of these same issues of much smaller nations of having too many people and not enough food production areas. They're also really interested in incorporating data science and traceability into the agricultural landscape. Recently, a new set of guidelines the Food Safety Modernization Act has been passed and so there are really new strict regulations on what kinds of food can and cannot be sold and imported in the United States. And so in order for foreign farmers to be able to plug their products into the United States market, they're going to have to meet these standards. Traceability is a huge part of that and so data science can also really help leverage that and so that's one of the large interests coming from China when it comes to data science in agriculture. I can see the value of it because you're going to want to know the food that's coming into the food stream basically meets certain standards and has enough of X and not too much of Y or whatever they may be pesticides or residues of this kind of chemical or that. Or was post-harvest stored at the right temperature for the right amount of time that it's safe for consumption. It really will allow a very fine detailed tracking on products. I can see that could be very much a valuable part of it. One kind of fun new tracking technology that I'm just kind of learning about is stickers that actually will be able to have temperature sensors in the stickers. So imagine being able to slap that on a box and measure what temperature that food is kept at for its entire life cycle. Yes, I have those actually are being used in some industries already for, I think in the pharmaceutical industry use them because if some medications need to be kept refrigerated and these tags will basically turn red if the temperature has not been kept cold, if they've gotten too warm once and so lets you know right away. To begin to integrate that again, as you say, this being able to meld the data is so critical now. Again, it's actually a lot of parallels now that I think about it. Tesla, for instance now has all these their new cars have always on internet connections and have huge arrays of sensors on them and Tesla is gathering all this data from all these cars being driven around now and using that data to inform how they build the next generation of cars, right? But again, it's pulling driving habits together with road conditions with wear and tear on the cars all these different kinds of information are now available that were not available before. You would love to be the Tesla of agriculture in science. I'm going to go ahead. That's a very admirable sort of model in a sense, right? To see that idea and it sounds like they're really on to that. What about the production of the you say that you call this an app but there's clearly got to be a certain amount of hardware associated with it, right? So we can actually fit in with any hardware on the market that's where hardware agnostic means. We're also in the process too of developing better hardware too that has longer transmission distances so that it really solves the connectivity issues that a lot of farms have. If they don't have Wi-Fi it could be difficult to pull that data to the cloud. So we are working on developing some better long range transmission sensors. Yeah. We were just in a team meeting earlier today really discussing the hardware aspect of it and some of the issues that we're already encountering with some of the farmers such as connectivity and power. We have a sensor out in the field, out in the garden but if there isn't power and there isn't access to Wi-Fi how is that sensor going to interact with our platform? It's not unless we have something. So we started brainstorming what if we got a small solar unit attached to the sensor and then had 3G running with that so we're excited about the future and we're constantly brainstorming different ways to tap into the current hardware needs and also like Kristin said thinking about future hardware needs and how our team can work to do that. Yeah, I suspect it must be tied into the whole solar photovoltaic stuff because that's an obvious source for the power of the remote sensors in pricking of farms which depend on sunlight, right? And yeah, as you said, you have to also get that connectivity issue. That would be the hard part in some of these remote Pacific islands because the connectivity is so lousy in those places but more of the world now is getting so much better connected every day not just in the US but a lot of other parts of the world so it's going to be very exciting to see where this goes. It's I don't know much about SmartYields but does your CEO he must have a business plan, right? What does he see in five years? Where is SmartYields going to be in five years? Five years? Wow, that's a bit down the road. I mean, you know if we could be the Tesla of data science and agriculture, that would be amazing. I think as we continue to aggregate the data and have it inform our practices and continue to develop better hardware solutions really having SmartYields and interacting with a bunch of other technologies to really come together and like we were saying for the why at the beginning and by 2050 our world's population is increasing by 40%. That is crazy and so really coming together working with a lot of different partners throughout the world to make it a sustainable future for everyone involved. I was thinking when we were talking about bringing this team together you must have a lot of very diverse backgrounds there because you've got to have the people who know the sensors, you've got to have the people who understand the app end of it. You've got to have people who can talk to both of those people and do the computer stuff in between us, right? You've got to have people who actually know farming. We have farmers on board. We have hardware developers. We have software developers. We've got to have that whole slew. Business gurus. We're always looking for more hardware and software tech savvy people who want to join the team. Finding the right talent is honestly the biggest challenge that we face. That's believable. You're really working out there on what they sometimes call the bleeding edge, right? That's a very dangerous place to be because it often is it's hard to get the right people involved with the right kind of energy, the right kind of knowledge, the right kind of set of skills. Very exciting. That's wonderful. You guys are well set here in terms of your careers, right? We're having a lot of fun. We're learning so much and so happy to be a part of a growing, exciting, relevant, relevant group of people. Excellent. Tell me as you wrap things up what would your advice be to the students of today who might want to get into this kind of thing for tomorrow? What kinds of courses should they pursue? What kinds of learning should they undertake? Definitely, as Kristin and I were saying, data science. That is so big. Not just in the agriculture industry, but across all industries. Data science is one of the next biggest things. Huge. And I would just really encourage students that when you're in classrooms to be as engaged as possible, it's an amazing opportunity to be a student. I know because I just graduated and I already miss it a little bit. As much as I love smart yields, of course. But to really ask good questions and to always seek out opportunities to get those hands-on experiences. So go visit a farm, go pull weeds, go volunteer in the low E. That's the other thing that I would really recommend to students. Getting back to just for a moment with the data science, UH Hilo is actually starting a whole data science program now. They're going to be hiring data science faculty and really running us all in connection with another big project they're doing here. So it's very, very exciting. Yes, very exciting. We actually, me and Ryan, one of the other members of the team, we were at UH Hilo a couple weeks ago and we met with the drone researchers there and we were discussing potential opportunities for collaboration in the future. You can get on with the guy who does drones here. Anyhow, I think we're about running our time. So, Lizzie Kristen, thank you so much for being here. I wish you the best of luck with smart yields. Sounds like you're going great guns. And I hope you'll come back and join us next week on another round of likeable science here on Friday at 2pm. Bye-bye.